Line data Source code
1 : /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2 : Copyright (c) 2011-2023 The plumed team
3 : (see the PEOPLE file at the root of the distribution for a list of names)
4 :
5 : See http://www.plumed.org for more information.
6 :
7 : This file is part of plumed, version 2.
8 :
9 : plumed is free software: you can redistribute it and/or modify
10 : it under the terms of the GNU Lesser General Public License as published by
11 : the Free Software Foundation, either version 3 of the License, or
12 : (at your option) any later version.
13 :
14 : plumed is distributed in the hope that it will be useful,
15 : but WITHOUT ANY WARRANTY; without even the implied warranty of
16 : MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17 : GNU Lesser General Public License for more details.
18 :
19 : You should have received a copy of the GNU Lesser General Public License
20 : along with plumed. If not, see <http://www.gnu.org/licenses/>.
21 : +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
22 : #include "Bias.h"
23 : #include "ActionRegister.h"
24 : #include "core/ActionSet.h"
25 : #include "core/PlumedMain.h"
26 : #include "core/Atoms.h"
27 : #include "core/FlexibleBin.h"
28 : #include "tools/Exception.h"
29 : #include "tools/Grid.h"
30 : #include "tools/Matrix.h"
31 : #include "tools/OpenMP.h"
32 : #include "tools/Random.h"
33 : #include "tools/File.h"
34 : #include <ctime>
35 : #include <numeric>
36 : #if defined(__PLUMED_HAS_GETCWD)
37 : #include <unistd.h>
38 : #endif
39 :
40 : namespace PLMD {
41 : namespace bias {
42 :
43 : //+PLUMEDOC BIAS METAD
44 : /*
45 : Used to performed metadynamics on one or more collective variables.
46 :
47 : In a metadynamics simulations a history dependent bias composed of
48 : intermittently added Gaussian functions is added to the potential \cite metad.
49 :
50 : \f[
51 : V(\vec{s},t) = \sum_{ k \tau < t} W(k \tau)
52 : \exp\left(
53 : -\sum_{i=1}^{d} \frac{(s_i-s_i^{(0)}(k \tau))^2}{2\sigma_i^2}
54 : \right).
55 : \f]
56 :
57 : This potential forces the system away from the kinetic traps in the potential energy surface
58 : and out into the unexplored parts of the energy landscape. Information on the Gaussian
59 : functions from which this potential is composed is output to a file called HILLS, which
60 : is used both the restart the calculation and to reconstruct the free energy as a function of the CVs.
61 : The free energy can be reconstructed from a metadynamics calculation because the final bias is given
62 : by:
63 :
64 : \f[
65 : V(\vec{s}) = -F(\vec{s})
66 : \f]
67 :
68 : During post processing the free energy can be calculated in this way using the \ref sum_hills
69 : utility.
70 :
71 : In the simplest possible implementation of a metadynamics calculation the expense of a metadynamics
72 : calculation increases with the length of the simulation as one has to, at every step, evaluate
73 : the values of a larger and larger number of Gaussian kernels. To avoid this issue you can
74 : store the bias on a grid. This approach is similar to that proposed in \cite babi08jcp but has the
75 : advantage that the grid spacing is independent on the Gaussian width.
76 : Notice that you should provide the grid boundaries (GRID_MIN and GRID_MAX) and either the number of bins
77 : for every collective variable (GRID_BIN) or the desired grid spacing (GRID_SPACING).
78 : In case you provide both PLUMED will use the most conservative choice (highest number of bins) for each dimension.
79 : In case you do not provide any information about bin size (neither GRID_BIN nor GRID_SPACING)
80 : PLUMED will use 1/5 of the Gaussian width (SIGMA) as grid spacing if the width is fixed or 1/5 of the minimum
81 : Gaussian width (SIGMA_MIN) if the width is variable. This default choice should be reasonable for most applications.
82 :
83 : Alternatively to the use of grids, it is possible to use a neighbor list to decrease the cost of evaluating the bias,
84 : this can be enabled using NLIST. NLIST can be beneficial with more than 2 collective variables, where GRID becomes
85 : expensive and memory consuming. The neighbor list will be updated everytime the CVs go farther than a cut-off value
86 : from the position they were at last neighbor list update. Gaussians are added to the neigbhor list if their center
87 : is within 6.*DP2CUTOFF*sigma*sigma. While the list is updated if the CVs are farther from the center than 0.5 of the
88 : standard deviation of the Gaussian center distribution of the list. These parameters (6 and 0.5) can be modified using
89 : NLIST_PARAMETERS. Note that the use of neighbor list does not provide the exact bias.
90 :
91 : Metadynamics can be restarted either from a HILLS file as well as from a GRID, in this second
92 : case one can first save a GRID using GRID_WFILE (and GRID_WSTRIDE) and at a later stage read
93 : it using GRID_RFILE.
94 :
95 : The work performed by the METAD bias can be calculated using CALC_WORK, note that this is expensive when not using grids.
96 :
97 : Another option that is available in plumed is well-tempered metadynamics \cite Barducci:2008. In this
98 : variant of metadynamics the heights of the Gaussian hills are scaled at each step so the bias is now
99 : given by:
100 :
101 : \f[
102 : V({s},t)= \sum_{t'=0,\tau_G,2\tau_G,\dots}^{t'<t} W e^{-V({s}({q}(t'),t')/\Delta T} \exp\left(
103 : -\sum_{i=1}^{d} \frac{(s_i({q})-s_i({q}(t'))^2}{2\sigma_i^2}
104 : \right),
105 : \f]
106 :
107 : This method ensures that the bias converges more smoothly. It should be noted that, in the case of well-tempered metadynamics, in
108 : the output printed the Gaussian height is re-scaled using the bias factor.
109 : Also notice that with well-tempered metadynamics the HILLS file does not contain the bias,
110 : but the negative of the free-energy estimate. This choice has the advantage that
111 : one can restart a simulation using a different value for the \f$\Delta T\f$. The applied bias will be scaled accordingly.
112 :
113 : Note that you can use here also the flexible Gaussian approach \cite Branduardi:2012dl
114 : in which you can adapt the Gaussian to the extent of Cartesian space covered by a variable or
115 : to the space in collective variable covered in a given time. In this case the width of the deposited
116 : Gaussian potential is denoted by one value only that is a Cartesian space (ADAPTIVE=GEOM) or a time
117 : (ADAPTIVE=DIFF). Note that a specific integration technique for the deposited Gaussian kernels
118 : should be used in this case. Check the documentation for utility sum_hills.
119 :
120 : With the keyword INTERVAL one changes the metadynamics algorithm setting the bias force equal to zero
121 : outside boundary \cite baftizadeh2012protein. If, for example, metadynamics is performed on a CV s and one is interested only
122 : to the free energy for s > boundary, the history dependent potential is still updated according to the above
123 : equations but the metadynamics force is set to zero for s < boundary. Notice that Gaussian kernels are added also
124 : if s < boundary, as the tails of these Gaussian kernels influence VG in the relevant region s > boundary. In this way, the
125 : force on the system in the region s > boundary comes from both metadynamics and the force field, in the region
126 : s < boundary only from the latter. This approach allows obtaining a history-dependent bias potential VG that
127 : fluctuates around a stable estimator, equal to the negative of the free energy far enough from the
128 : boundaries. Note that:
129 : - It works only for one-dimensional biases;
130 : - It works both with and without GRID;
131 : - The interval limit boundary in a region where the free energy derivative is not large;
132 : - If in the region outside the limit boundary the system has a free energy minimum, the INTERVAL keyword should
133 : be used together with a \ref UPPER_WALLS or \ref LOWER_WALLS at boundary.
134 :
135 : As a final note, since version 2.0.2 when the system is outside of the selected interval the force
136 : is set to zero and the bias value to the value at the corresponding boundary. This allows acceptances
137 : for replica exchange methods to be computed correctly.
138 :
139 : Multiple walkers \cite multiplewalkers can also be used. See below the examples.
140 :
141 :
142 : The \f$c(t)\f$ reweighting factor can also be calculated on the fly using the equations
143 : presented in \cite Tiwary_jp504920s.
144 : The expression used to calculate \f$c(t)\f$ follows directly from Eq. 3 in \cite Tiwary_jp504920s,
145 : where \f$F(\vec{s})=-\gamma/(\gamma-1) V(\vec{s})\f$.
146 : This gives smoother results than equivalent Eqs. 13 and Eqs. 14 in that paper.
147 : The \f$c(t)\f$ is given by the rct component while the bias
148 : normalized by \f$c(t)\f$ is given by the rbias component (rbias=bias-rct) which can be used
149 : to obtain a reweighted histogram.
150 : The calculation of \f$c(t)\f$ is enabled by using the keyword CALC_RCT.
151 : By default \f$c(t)\f$ is updated every time the bias changes, but if this slows down the simulation
152 : the keyword RCT_USTRIDE can be set to a value higher than 1.
153 : This option requires that a grid is used.
154 :
155 : Additional material and examples can be also found in the tutorials:
156 :
157 : - \ref lugano-3
158 :
159 : Concurrent metadynamics
160 : as done e.g. in Ref. \cite gil2015enhanced . This indeed can be obtained by using the METAD
161 : action multiple times in the same input file.
162 :
163 : \par Examples
164 :
165 : The following input is for a standard metadynamics calculation using as
166 : collective variables the distance between atoms 3 and 5
167 : and the distance between atoms 2 and 4. The value of the CVs and
168 : the metadynamics bias potential are written to the COLVAR file every 100 steps.
169 : \plumedfile
170 : DISTANCE ATOMS=3,5 LABEL=d1
171 : DISTANCE ATOMS=2,4 LABEL=d2
172 : METAD ARG=d1,d2 SIGMA=0.2,0.2 HEIGHT=0.3 PACE=500 LABEL=restraint
173 : PRINT ARG=d1,d2,restraint.bias STRIDE=100 FILE=COLVAR
174 : \endplumedfile
175 : (See also \ref DISTANCE \ref PRINT).
176 :
177 : \par
178 : If you use adaptive Gaussian kernels, with diffusion scheme where you use
179 : a Gaussian that should cover the space of 20 time steps in collective variables.
180 : Note that in this case the histogram correction is needed when summing up hills.
181 : \plumedfile
182 : DISTANCE ATOMS=3,5 LABEL=d1
183 : DISTANCE ATOMS=2,4 LABEL=d2
184 : METAD ARG=d1,d2 SIGMA=20 HEIGHT=0.3 PACE=500 LABEL=restraint ADAPTIVE=DIFF
185 : PRINT ARG=d1,d2,restraint.bias STRIDE=100 FILE=COLVAR
186 : \endplumedfile
187 :
188 : \par
189 : If you use adaptive Gaussian kernels, with geometrical scheme where you use
190 : a Gaussian that should cover the space of 0.05 nm in Cartesian space.
191 : Note that in this case the histogram correction is needed when summing up hills.
192 : \plumedfile
193 : DISTANCE ATOMS=3,5 LABEL=d1
194 : DISTANCE ATOMS=2,4 LABEL=d2
195 : METAD ARG=d1,d2 SIGMA=0.05 HEIGHT=0.3 PACE=500 LABEL=restraint ADAPTIVE=GEOM
196 : PRINT ARG=d1,d2,restraint.bias STRIDE=100 FILE=COLVAR
197 : \endplumedfile
198 :
199 : \par
200 : When using adaptive Gaussian kernels you might want to limit how the hills width can change.
201 : You can use SIGMA_MIN and SIGMA_MAX keywords.
202 : The sigmas should specified in terms of CV so you should use the CV units.
203 : Note that if you use a negative number, this means that the limit is not set.
204 : Note also that in this case the histogram correction is needed when summing up hills.
205 : \plumedfile
206 : DISTANCE ATOMS=3,5 LABEL=d1
207 : DISTANCE ATOMS=2,4 LABEL=d2
208 : METAD ...
209 : ARG=d1,d2 SIGMA=0.05 HEIGHT=0.3 PACE=500 LABEL=restraint ADAPTIVE=GEOM
210 : SIGMA_MIN=0.2,0.1 SIGMA_MAX=0.5,1.0
211 : ... METAD
212 : PRINT ARG=d1,d2,restraint.bias STRIDE=100 FILE=COLVAR
213 : \endplumedfile
214 :
215 : \par
216 : Multiple walkers can be also use as in \cite multiplewalkers
217 : These are enabled by setting the number of walker used, the id of the
218 : current walker which interprets the input file, the directory where the
219 : hills containing files resides, and the frequency to read the other walkers.
220 : Here is an example
221 : \plumedfile
222 : DISTANCE ATOMS=3,5 LABEL=d1
223 : METAD ...
224 : ARG=d1 SIGMA=0.05 HEIGHT=0.3 PACE=500 LABEL=restraint
225 : WALKERS_N=10
226 : WALKERS_ID=3
227 : WALKERS_DIR=../
228 : WALKERS_RSTRIDE=100
229 : ... METAD
230 : \endplumedfile
231 : where WALKERS_N is the total number of walkers, WALKERS_ID is the
232 : id of the present walker (starting from 0 ) and the WALKERS_DIR is the directory
233 : where all the walkers are located. WALKERS_RSTRIDE is the number of step between
234 : one update and the other. Since version 2.2.5, hills files are automatically
235 : flushed every WALKERS_RSTRIDE steps.
236 :
237 : \par
238 : The \f$c(t)\f$ reweighting factor can be calculated on the fly using the equations
239 : presented in \cite Tiwary_jp504920s as described above.
240 : This is enabled by using the keyword CALC_RCT,
241 : and can be done only if the bias is defined on a grid.
242 : \plumedfile
243 : phi: TORSION ATOMS=1,2,3,4
244 : psi: TORSION ATOMS=5,6,7,8
245 :
246 : METAD ...
247 : LABEL=metad
248 : ARG=phi,psi SIGMA=0.20,0.20 HEIGHT=1.20 BIASFACTOR=5 TEMP=300.0 PACE=500
249 : GRID_MIN=-pi,-pi GRID_MAX=pi,pi GRID_BIN=150,150
250 : CALC_RCT
251 : RCT_USTRIDE=10
252 : ... METAD
253 : \endplumedfile
254 : Here we have asked that the calculation is performed every 10 hills deposition by using
255 : RCT_USTRIDE keyword. If this keyword is not given, the calculation will
256 : by default be performed every time the bias changes. The \f$c(t)\f$ reweighting factor will be given
257 : in the rct component while the instantaneous value of the bias potential
258 : normalized using the \f$c(t)\f$ reweighting factor is given in the rbias component
259 : [rbias=bias-rct] which can be used to obtain a reweighted histogram or
260 : free energy surface using the \ref HISTOGRAM analysis.
261 :
262 : \par
263 : The kinetics of the transitions between basins can also be analyzed on the fly as
264 : in \cite PRL230602. The flag ACCELERATION turn on accumulation of the acceleration
265 : factor that can then be used to determine the rate. This method can be used together
266 : with \ref COMMITTOR analysis to stop the simulation when the system get to the target basin.
267 : It must be used together with Well-Tempered Metadynamics. If restarting from a previous
268 : metadynamics you need to use the ACCELERATION_RFILE keyword to give the name of the
269 : data file from which the previous value of the acceleration factor should be read, otherwise the
270 : calculation of the acceleration factor will be wrong.
271 :
272 : \par
273 : By using the flag FREQUENCY_ADAPTIVE the frequency adaptive scheme introduced in \cite Wang-JCP-2018
274 : is turned on. The frequency for hill addition then changes dynamically based on the acceleration factor
275 : according to the following equation
276 : \f[
277 : \tau_{\mathrm{dep}}(t) =
278 : \min\left[
279 : \tau_0 \cdot
280 : \max\left[\frac{\alpha(t)}{\theta},1\right]
281 : ,\tau_{c}
282 : \right]
283 : \f]
284 : where \f$\tau_0\f$ is the initial hill addition frequency given by the PACE keyword,
285 : \f$\tau_{c}\f$ is the maximum allowed frequency given by the FA_MAX_PACE keyword,
286 : \f$\alpha(t)\f$ is the instantaneous acceleration factor at time \f$t\f$,
287 : and \f$\theta\f$ is a threshold value that acceleration factor has to reach before
288 : triggering a change in the hill addition frequency given by the FA_MIN_ACCELERATION keyword.
289 : The frequency for updating the hill addition frequency according to this equation is
290 : given by the FA_UPDATE_FREQUENCY keyword, by default it is the same as the value given
291 : in PACE. The hill hill addition frequency increase monotonously such that if the
292 : instantaneous acceleration factor is lower than in the previous updating step the
293 : previous \f$\tau_{\mathrm{dep}}\f$ is kept rather than updating it to a lower value.
294 : The instantaneous hill addition frequency \f$\tau_{\mathrm{dep}}(t)\f$ is outputted
295 : to pace component. Note that if restarting from a previous metadynamics run you need to
296 : use the ACCELERATION_RFILE keyword to read in the acceleration factors from the
297 : previous run, otherwise the hill addition frequency will start from the initial
298 : frequency.
299 :
300 :
301 : \par
302 : You can also provide a target distribution using the keyword TARGET
303 : \cite white2015designing
304 : \cite marinelli2015ensemble
305 : \cite gil2016empirical
306 : The TARGET should be a grid containing a free-energy (i.e. the -\f$k_B\f$T*log of the desired target distribution).
307 : Gaussian kernels will then be scaled by a factor
308 : \f[
309 : e^{\beta(\tilde{F}(s)-\tilde{F}_{max})}
310 : \f]
311 : Here \f$\tilde{F}(s)\f$ is the free energy defined on the grid and \f$\tilde{F}_{max}\f$ its maximum value.
312 : Notice that we here used the maximum value as in ref \cite gil2016empirical
313 : This choice allows to avoid exceedingly large Gaussian kernels to be added. However,
314 : it could make the Gaussian too small. You should always choose carefully the HEIGHT parameter
315 : in this case.
316 : The grid file should be similar to other PLUMED grid files in that it should contain
317 : both the target free-energy and its derivatives.
318 :
319 : Notice that if you wish your simulation to converge to the target free energy you should use
320 : the DAMPFACTOR command to provide a global tempering \cite dama2014well
321 : Alternatively, if you use a BIASFACTOR your simulation will converge to a free
322 : energy that is a linear combination of the target free energy and of the intrinsic free energy
323 : determined by the original force field.
324 :
325 : \plumedfile
326 : DISTANCE ATOMS=3,5 LABEL=d1
327 : METAD ...
328 : LABEL=t1
329 : ARG=d1 SIGMA=0.05 TAU=200 DAMPFACTOR=100 PACE=250
330 : GRID_MIN=1.14 GRID_MAX=1.32 GRID_BIN=6
331 : TARGET=dist.grid
332 : ... METAD
333 :
334 : PRINT ARG=d1,t1.bias STRIDE=100 FILE=COLVAR
335 : \endplumedfile
336 :
337 : The file dist.dat for this calculation would read:
338 :
339 : \auxfile{dist.grid}
340 : #! FIELDS d1 t1.target der_d1
341 : #! SET min_d1 1.14
342 : #! SET max_d1 1.32
343 : #! SET nbins_d1 6
344 : #! SET periodic_d1 false
345 : 1.1400 0.0031 0.1101
346 : 1.1700 0.0086 0.2842
347 : 1.2000 0.0222 0.6648
348 : 1.2300 0.0521 1.4068
349 : 1.2600 0.1120 2.6873
350 : 1.2900 0.2199 4.6183
351 : 1.3200 0.3948 7.1055
352 : \endauxfile
353 :
354 : Notice that BIASFACTOR can also be chosen as equal to 1. In this case one will perform
355 : unbiased sampling. Instead of using HEIGHT, one should provide the TAU parameter.
356 : \plumedfile
357 : d: DISTANCE ATOMS=3,5
358 : METAD ARG=d SIGMA=0.1 TAU=4.0 TEMP=300 PACE=100 BIASFACTOR=1.0
359 : \endplumedfile
360 : The HILLS file obtained will still work with `plumed sum_hills` so as to plot a free-energy.
361 : The case where this makes sense is probably that of RECT simulations.
362 :
363 : Regarding RECT simulations, you can also use the RECT keyword so as to avoid using multiple input files.
364 : For instance, a single input file will be
365 : \plumedfile
366 : d: DISTANCE ATOMS=3,5
367 : METAD ARG=d SIGMA=0.1 TAU=4.0 TEMP=300 PACE=100 RECT=1.0,1.5,2.0,3.0
368 : \endplumedfile
369 : The number of elements in the RECT array should be equal to the number of replicas.
370 :
371 : */
372 : //+ENDPLUMEDOC
373 :
374 : class MetaD : public Bias {
375 :
376 : private:
377 : struct Gaussian {
378 : bool multivariate; // this is required to discriminate the one dimensional case
379 : double height;
380 : std::vector<double> center;
381 : std::vector<double> sigma;
382 : std::vector<double> invsigma;
383 5200 : Gaussian(const bool m, const double h, const std::vector<double>& c, const std::vector<double>& s):
384 5200 : multivariate(m),height(h),center(c),sigma(s),invsigma(s) {
385 : // to avoid troubles from zero element in flexible hills
386 15137 : for(unsigned i=0; i<invsigma.size(); ++i) {
387 9937 : if(std::abs(invsigma[i])>1.e-20) invsigma[i]=1.0/invsigma[i] ;
388 0 : else invsigma[i]=0.0;
389 : }
390 5200 : }
391 : };
392 : struct TemperingSpecs {
393 : bool is_active;
394 : std::string name_stem;
395 : std::string name;
396 : double biasf;
397 : double threshold;
398 : double alpha;
399 154 : inline TemperingSpecs(bool is_active, const std::string &name_stem, const std::string &name, double biasf, double threshold, double alpha) :
400 154 : is_active(is_active), name_stem(name_stem), name(name), biasf(biasf), threshold(threshold), alpha(alpha)
401 154 : {}
402 : };
403 : // general setup
404 : double kbt_;
405 : int stride_;
406 : bool calc_work_;
407 : // well-tempered MetaD
408 : bool welltemp_;
409 : double biasf_;
410 : // output files format
411 : std::string fmt_;
412 : // first step?
413 : bool isFirstStep_;
414 : // Gaussian starting parameters
415 : double height0_;
416 : std::vector<double> sigma0_;
417 : std::vector<double> sigma0min_;
418 : std::vector<double> sigma0max_;
419 : // Gaussians
420 : std::vector<Gaussian> hills_;
421 : std::unique_ptr<FlexibleBin> flexbin_;
422 : int adaptive_;
423 : OFile hillsOfile_;
424 : std::vector<std::unique_ptr<IFile>> ifiles_;
425 : std::vector<std::string> ifilesnames_;
426 : // Grids
427 : bool grid_;
428 : std::unique_ptr<GridBase> BiasGrid_;
429 : OFile gridfile_;
430 : bool storeOldGrids_;
431 : int wgridstride_;
432 : // multiple walkers
433 : int mw_n_;
434 : std::string mw_dir_;
435 : int mw_id_;
436 : int mw_rstride_;
437 : bool walkers_mpi_;
438 : unsigned mpi_nw_;
439 : // flying gaussians
440 : bool flying_;
441 : // kinetics from metadynamics
442 : bool acceleration_;
443 : double acc_;
444 : double acc_restart_mean_;
445 : // transition-tempering metadynamics
446 : bool calc_max_bias_;
447 : double max_bias_;
448 : bool calc_transition_bias_;
449 : double transition_bias_;
450 : std::vector<std::vector<double> > transitionwells_;
451 : static const size_t n_tempering_options_ = 1;
452 : static const std::string tempering_names_[1][2];
453 : double dampfactor_;
454 : struct TemperingSpecs tt_specs_;
455 : std::string targetfilename_;
456 : std::unique_ptr<GridBase> TargetGrid_;
457 : // frequency adaptive metadynamics
458 : int current_stride_;
459 : bool freq_adaptive_;
460 : int fa_update_frequency_;
461 : int fa_max_stride_;
462 : double fa_min_acceleration_;
463 : // intervals
464 : double uppI_;
465 : double lowI_;
466 : bool doInt_;
467 : // reweighting
468 : bool calc_rct_;
469 : double reweight_factor_;
470 : unsigned rct_ustride_;
471 : // work
472 : double work_;
473 : // neighbour list stuff
474 : bool nlist_;
475 : bool nlist_update_;
476 : unsigned nlist_steps_;
477 : std::array<double,2> nlist_param_;
478 : std::vector<Gaussian> nlist_hills_;
479 : std::vector<double> nlist_center_;
480 : std::vector<double> nlist_dev2_;
481 :
482 : double stretchA=1.0;
483 : double stretchB=0.0;
484 :
485 : bool noStretchWarningDone=false;
486 :
487 12 : void noStretchWarning() {
488 12 : if(!noStretchWarningDone) {
489 3 : log<<"\nWARNING: you are using a HILLS file with Gaussian kernels, PLUMED 2.8 uses stretched Gaussians by default\n";
490 : }
491 12 : noStretchWarningDone=true;
492 12 : }
493 :
494 : static void registerTemperingKeywords(const std::string &name_stem, const std::string &name, Keywords &keys);
495 : void readTemperingSpecs(TemperingSpecs &t_specs);
496 : void logTemperingSpecs(const TemperingSpecs &t_specs);
497 : void readGaussians(IFile*);
498 : void writeGaussian(const Gaussian&,OFile&);
499 : void addGaussian(const Gaussian&);
500 : double getHeight(const std::vector<double>&);
501 : void temperHeight(double &height, const TemperingSpecs &t_specs, const double tempering_bias);
502 : double getBias(const std::vector<double>&);
503 : double getBiasAndDerivatives(const std::vector<double>&, std::vector<double>&);
504 : double evaluateGaussian(const std::vector<double>&, const Gaussian&);
505 : double evaluateGaussianAndDerivatives(const std::vector<double>&, const Gaussian&,std::vector<double>&,std::vector<double>&);
506 : double getGaussianNormalization(const Gaussian&);
507 : std::vector<unsigned> getGaussianSupport(const Gaussian&);
508 : bool scanOneHill(IFile* ifile, std::vector<Value>& v, std::vector<double>& center, std::vector<double>& sigma, double& height, bool& multivariate);
509 : void computeReweightingFactor();
510 : double getTransitionBarrierBias();
511 : void updateFrequencyAdaptiveStride();
512 : void updateNlist();
513 :
514 : public:
515 : explicit MetaD(const ActionOptions&);
516 : void calculate() override;
517 : void update() override;
518 : static void registerKeywords(Keywords& keys);
519 : bool checkNeedsGradients()const override;
520 : };
521 :
522 10721 : PLUMED_REGISTER_ACTION(MetaD,"METAD")
523 :
524 156 : void MetaD::registerKeywords(Keywords& keys) {
525 156 : Bias::registerKeywords(keys);
526 312 : keys.addOutputComponent("rbias","CALC_RCT","the instantaneous value of the bias normalized using the \\f$c(t)\\f$ reweighting factor [rbias=bias-rct]."
527 : "This component can be used to obtain a reweighted histogram.");
528 312 : keys.addOutputComponent("rct","CALC_RCT","the reweighting factor \\f$c(t)\\f$.");
529 312 : keys.addOutputComponent("work","CALC_WORK","accumulator for work");
530 312 : keys.addOutputComponent("acc","ACCELERATION","the metadynamics acceleration factor");
531 312 : keys.addOutputComponent("maxbias", "CALC_MAX_BIAS", "the maximum of the metadynamics V(s, t)");
532 468 : keys.addOutputComponent("transbias", "CALC_TRANSITION_BIAS", "the metadynamics transition bias V*(t)");
533 468 : keys.addOutputComponent("pace","FREQUENCY_ADAPTIVE","the hill addition frequency when employing frequency adaptive metadynamics");
534 312 : keys.addOutputComponent("nlker","NLIST","number of hills in the neighbor list");
535 312 : keys.addOutputComponent("nlsteps","NLIST","number of steps from last neighbor list update");
536 156 : keys.use("ARG");
537 312 : keys.add("compulsory","SIGMA","the widths of the Gaussian hills");
538 312 : keys.add("compulsory","PACE","the frequency for hill addition");
539 312 : keys.add("compulsory","FILE","HILLS","a file in which the list of added hills is stored");
540 312 : keys.add("optional","HEIGHT","the heights of the Gaussian hills. Compulsory unless TAU and either BIASFACTOR or DAMPFACTOR are given");
541 312 : keys.add("optional","FMT","specify format for HILLS files (useful for decrease the number of digits in regtests)");
542 312 : keys.add("optional","BIASFACTOR","use well tempered metadynamics and use this bias factor. Please note you must also specify temp");
543 312 : keys.addFlag("CALC_WORK",false,"calculate the total accumulated work done by the bias since last restart");
544 312 : keys.add("optional","RECT","list of bias factors for all the replicas");
545 312 : keys.add("optional","DAMPFACTOR","damp hills with exp(-max(V)/(\\f$k_B\\f$T*DAMPFACTOR)");
546 312 : for (size_t i = 0; i < n_tempering_options_; i++) {
547 156 : registerTemperingKeywords(tempering_names_[i][0], tempering_names_[i][1], keys);
548 : }
549 312 : keys.add("optional","TARGET","target to a predefined distribution");
550 312 : keys.add("optional","TEMP","the system temperature - this is only needed if you are doing well-tempered metadynamics");
551 312 : keys.add("optional","TAU","in well tempered metadynamics, sets height to (\\f$k_B \\Delta T\\f$*pace*timestep)/tau");
552 312 : keys.addFlag("CALC_RCT",false,"calculate the \\f$c(t)\\f$ reweighting factor and use that to obtain the normalized bias [rbias=bias-rct]."
553 : "This method is not compatible with metadynamics not on a grid.");
554 312 : keys.add("optional","RCT_USTRIDE","the update stride for calculating the \\f$c(t)\\f$ reweighting factor."
555 : "The default 1, so \\f$c(t)\\f$ is updated every time the bias is updated.");
556 312 : keys.add("optional","GRID_MIN","the lower bounds for the grid");
557 312 : keys.add("optional","GRID_MAX","the upper bounds for the grid");
558 312 : keys.add("optional","GRID_BIN","the number of bins for the grid");
559 312 : keys.add("optional","GRID_SPACING","the approximate grid spacing (to be used as an alternative or together with GRID_BIN)");
560 312 : keys.addFlag("GRID_SPARSE",false,"use a sparse grid to store hills");
561 312 : keys.addFlag("GRID_NOSPLINE",false,"don't use spline interpolation with grids");
562 312 : keys.add("optional","GRID_WSTRIDE","write the grid to a file every N steps");
563 312 : keys.add("optional","GRID_WFILE","the file on which to write the grid");
564 312 : keys.add("optional","GRID_RFILE","a grid file from which the bias should be read at the initial step of the simulation");
565 312 : keys.addFlag("STORE_GRIDS",false,"store all the grid files the calculation generates. They will be deleted if this keyword is not present");
566 312 : keys.addFlag("NLIST",false,"Use neighbor list for kernels summation, faster but experimental");
567 468 : keys.add("optional", "NLIST_PARAMETERS","(default=6.,0.5) the two cutoff parameters for the Gaussians neighbor list");
568 312 : keys.add("optional","ADAPTIVE","use a geometric (=GEOM) or diffusion (=DIFF) based hills width scheme. Sigma is one number that has distance units or time step dimensions");
569 312 : keys.add("optional","SIGMA_MAX","the upper bounds for the sigmas (in CV units) when using adaptive hills. Negative number means no bounds ");
570 312 : keys.add("optional","SIGMA_MIN","the lower bounds for the sigmas (in CV units) when using adaptive hills. Negative number means no bounds ");
571 312 : keys.add("optional","WALKERS_ID", "walker id");
572 312 : keys.add("optional","WALKERS_N", "number of walkers");
573 312 : keys.add("optional","WALKERS_DIR", "shared directory with the hills files from all the walkers");
574 312 : keys.add("optional","WALKERS_RSTRIDE","stride for reading hills files");
575 312 : keys.addFlag("WALKERS_MPI",false,"Switch on MPI version of multiple walkers - not compatible with WALKERS_* options other than WALKERS_DIR");
576 312 : keys.add("optional","INTERVAL","one dimensional lower and upper limits, outside the limits the system will not feel the biasing force.");
577 312 : keys.addFlag("FLYING_GAUSSIAN",false,"Switch on flying Gaussian method, must be used with WALKERS_MPI");
578 312 : keys.addFlag("ACCELERATION",false,"Set to TRUE if you want to compute the metadynamics acceleration factor.");
579 468 : keys.add("optional","ACCELERATION_RFILE","a data file from which the acceleration should be read at the initial step of the simulation");
580 312 : keys.addFlag("CALC_MAX_BIAS", false, "Set to TRUE if you want to compute the maximum of the metadynamics V(s, t)");
581 312 : keys.addFlag("CALC_TRANSITION_BIAS", false, "Set to TRUE if you want to compute a metadynamics transition bias V*(t)");
582 312 : keys.add("numbered", "TRANSITIONWELL", "This keyword appears multiple times as TRANSITIONWELL followed by an integer. Each specifies the coordinates for one well as in transition-tempered metadynamics. At least one must be provided.");
583 312 : keys.addFlag("FREQUENCY_ADAPTIVE",false,"Set to TRUE if you want to enable frequency adaptive metadynamics such that the frequency for hill addition to change dynamically based on the acceleration factor.");
584 468 : keys.add("optional","FA_UPDATE_FREQUENCY","the frequency for updating the hill addition pace in frequency adaptive metadynamics, by default this is equal to the value given in PACE");
585 312 : keys.add("optional","FA_MAX_PACE","the maximum hill addition frequency allowed in frequency adaptive metadynamics. By default there is no maximum value.");
586 468 : keys.add("optional","FA_MIN_ACCELERATION","only update the hill addition pace in frequency adaptive metadynamics after reaching the minimum acceleration factor given here. By default it is 1.0.");
587 156 : keys.use("RESTART");
588 156 : keys.use("UPDATE_FROM");
589 156 : keys.use("UPDATE_UNTIL");
590 156 : }
591 :
592 : const std::string MetaD::tempering_names_[1][2] = {{"TT", "transition tempered"}};
593 :
594 156 : void MetaD::registerTemperingKeywords(const std::string &name_stem, const std::string &name, Keywords &keys) {
595 468 : keys.add("optional", name_stem + "BIASFACTOR", "use " + name + " metadynamics with this bias factor. Please note you must also specify temp");
596 468 : keys.add("optional", name_stem + "BIASTHRESHOLD", "use " + name + " metadynamics with this bias threshold. Please note you must also specify " + name_stem + "BIASFACTOR");
597 468 : keys.add("optional", name_stem + "ALPHA", "use " + name + " metadynamics with this hill size decay exponent parameter. Please note you must also specify " + name_stem + "BIASFACTOR");
598 156 : }
599 :
600 155 : MetaD::MetaD(const ActionOptions& ao):
601 : PLUMED_BIAS_INIT(ao),
602 154 : kbt_(0.0),
603 154 : stride_(0),
604 154 : calc_work_(false),
605 154 : welltemp_(false),
606 154 : biasf_(-1.0),
607 154 : isFirstStep_(true),
608 154 : height0_(std::numeric_limits<double>::max()),
609 154 : adaptive_(FlexibleBin::none),
610 154 : grid_(false),
611 154 : wgridstride_(0),
612 154 : mw_n_(1), mw_dir_(""), mw_id_(0), mw_rstride_(1),
613 154 : walkers_mpi_(false), mpi_nw_(0),
614 154 : flying_(false),
615 154 : acceleration_(false), acc_(0.0), acc_restart_mean_(0.0),
616 154 : calc_max_bias_(false), max_bias_(0.0),
617 154 : calc_transition_bias_(false), transition_bias_(0.0),
618 154 : dampfactor_(0.0),
619 308 : tt_specs_(false, "TT", "Transition Tempered", -1.0, 0.0, 1.0),
620 154 : current_stride_(0),
621 154 : freq_adaptive_(false),
622 154 : fa_update_frequency_(0),
623 154 : fa_max_stride_(0),
624 154 : fa_min_acceleration_(1.0),
625 154 : uppI_(-1), lowI_(-1), doInt_(false),
626 154 : calc_rct_(false),
627 154 : reweight_factor_(0.0),
628 154 : rct_ustride_(1),
629 154 : work_(0),
630 154 : nlist_(false),
631 154 : nlist_update_(false),
632 463 : nlist_steps_(0)
633 : {
634 154 : if(!dp2cutoffNoStretch()) {
635 154 : stretchA=dp2cutoffA;
636 154 : stretchB=dp2cutoffB;
637 : }
638 : // parse the flexible hills
639 : std::string adaptiveoption;
640 : adaptiveoption="NONE";
641 308 : parse("ADAPTIVE",adaptiveoption);
642 154 : if(adaptiveoption=="GEOM") {
643 22 : log.printf(" Uses Geometry-based hills width: sigma must be in distance units and only one sigma is needed\n");
644 22 : adaptive_=FlexibleBin::geometry;
645 132 : } else if(adaptiveoption=="DIFF") {
646 3 : log.printf(" Uses Diffusion-based hills width: sigma must be in time steps and only one sigma is needed\n");
647 3 : adaptive_=FlexibleBin::diffusion;
648 129 : } else if(adaptiveoption=="NONE") {
649 128 : adaptive_=FlexibleBin::none;
650 : } else {
651 1 : error("I do not know this type of adaptive scheme");
652 : }
653 :
654 153 : parse("FMT",fmt_);
655 :
656 : // parse the sigma
657 153 : parseVector("SIGMA",sigma0_);
658 153 : if(adaptive_==FlexibleBin::none) {
659 : // if you use normal sigma you need one sigma per argument
660 128 : if( sigma0_.size()!=getNumberOfArguments() ) error("number of arguments does not match number of SIGMA parameters");
661 : } else {
662 : // if you use flexible hills you need one sigma
663 25 : if(sigma0_.size()!=1) {
664 1 : error("If you choose ADAPTIVE you need only one sigma according to your choice of type (GEOM/DIFF)");
665 : }
666 : // if adaptive then the number must be an integer
667 24 : if(adaptive_==FlexibleBin::diffusion) {
668 3 : if(int(sigma0_[0])-sigma0_[0]>1.e-9 || int(sigma0_[0])-sigma0_[0] <-1.e-9 || int(sigma0_[0])<1 ) {
669 0 : error("In case of adaptive hills with diffusion, the sigma must be an integer which is the number of time steps\n");
670 : }
671 : }
672 : // here evtl parse the sigma min and max values
673 48 : parseVector("SIGMA_MIN",sigma0min_);
674 24 : if(sigma0min_.size()>0 && sigma0min_.size()!=getNumberOfArguments()) {
675 1 : error("the number of SIGMA_MIN values be the same of the number of the arguments");
676 23 : } else if(sigma0min_.size()==0) {
677 23 : sigma0min_.resize(getNumberOfArguments());
678 67 : for(unsigned i=0; i<getNumberOfArguments(); i++) {sigma0min_[i]=-1.;}
679 : }
680 :
681 46 : parseVector("SIGMA_MAX",sigma0max_);
682 23 : if(sigma0max_.size()>0 && sigma0max_.size()!=getNumberOfArguments()) {
683 1 : error("the number of SIGMA_MAX values be the same of the number of the arguments");
684 22 : } else if(sigma0max_.size()==0) {
685 22 : sigma0max_.resize(getNumberOfArguments());
686 64 : for(unsigned i=0; i<getNumberOfArguments(); i++) {sigma0max_[i]=-1.;}
687 : }
688 :
689 44 : flexbin_=Tools::make_unique<FlexibleBin>(adaptive_,this,sigma0_[0],sigma0min_,sigma0max_);
690 : }
691 :
692 : // note: HEIGHT is not compulsory, since one could use the TAU keyword, see below
693 150 : parse("HEIGHT",height0_);
694 150 : parse("PACE",stride_);
695 149 : if(stride_<=0) error("frequency for hill addition is nonsensical");
696 149 : current_stride_ = stride_;
697 156 : std::string hillsfname="HILLS";
698 149 : parse("FILE",hillsfname);
699 :
700 : // Manually set to calculate special bias quantities
701 : // throughout the course of simulation. (These are chosen due to
702 : // relevance for tempering and event-driven logic as well.)
703 149 : parseFlag("CALC_MAX_BIAS", calc_max_bias_);
704 300 : parseFlag("CALC_TRANSITION_BIAS", calc_transition_bias_);
705 :
706 : std::vector<double> rect_biasf_;
707 298 : parseVector("RECT",rect_biasf_);
708 149 : if(rect_biasf_.size()>0) {
709 18 : int r=0;
710 18 : if(comm.Get_rank()==0) r=multi_sim_comm.Get_rank();
711 18 : comm.Bcast(r,0);
712 18 : biasf_=rect_biasf_[r];
713 18 : log<<" You are using RECT\n";
714 : } else {
715 262 : parse("BIASFACTOR",biasf_);
716 : }
717 149 : if( biasf_<1.0 && biasf_!=-1.0) error("well tempered bias factor is nonsensical");
718 149 : parse("DAMPFACTOR",dampfactor_);
719 149 : double temp=0.0;
720 149 : parse("TEMP",temp);
721 149 : if(temp>0.0) kbt_=plumed.getAtoms().getKBoltzmann()*temp;
722 94 : else kbt_=plumed.getAtoms().getKbT();
723 149 : if(biasf_>=1.0) {
724 37 : if(kbt_==0.0) error("Unless the MD engine passes the temperature to plumed, with well-tempered metad you must specify it using TEMP");
725 37 : welltemp_=true;
726 : }
727 149 : if(dampfactor_>0.0) {
728 2 : if(kbt_==0.0) error("Unless the MD engine passes the temperature to plumed, with damped metad you must specify it using TEMP");
729 : }
730 :
731 149 : parseFlag("CALC_WORK",calc_work_);
732 :
733 : // Set transition tempering parameters.
734 : // Transition wells are read later via calc_transition_bias_.
735 149 : readTemperingSpecs(tt_specs_);
736 149 : if (tt_specs_.is_active) calc_transition_bias_ = true;
737 :
738 : // If any previous option specified to calculate a transition bias,
739 : // now read the transition wells for that quantity.
740 149 : if (calc_transition_bias_) {
741 13 : std::vector<double> tempcoords(getNumberOfArguments());
742 26 : for (unsigned i = 0; ; i++) {
743 78 : if (!parseNumberedVector("TRANSITIONWELL", i, tempcoords) ) break;
744 26 : if (tempcoords.size() != getNumberOfArguments()) {
745 0 : error("incorrect number of coordinates for transition tempering well");
746 : }
747 26 : transitionwells_.push_back(tempcoords);
748 : }
749 : }
750 :
751 298 : parse("TARGET",targetfilename_);
752 149 : if(targetfilename_.length()>0 && kbt_==0.0) error("with TARGET temperature must be specified");
753 149 : double tau=0.0;
754 149 : parse("TAU",tau);
755 149 : if(tau==0.0) {
756 127 : if(height0_==std::numeric_limits<double>::max()) error("At least one between HEIGHT and TAU should be specified");
757 : // if tau is not set, we compute it here from the other input parameters
758 127 : if(welltemp_) tau=(kbt_*(biasf_-1.0))/height0_*getTimeStep()*stride_;
759 109 : else if(dampfactor_>0.0) tau=(kbt_*dampfactor_)/height0_*getTimeStep()*stride_;
760 : } else {
761 22 : if(height0_!=std::numeric_limits<double>::max()) error("At most one between HEIGHT and TAU should be specified");
762 22 : if(welltemp_) {
763 19 : if(biasf_!=1.0) height0_=(kbt_*(biasf_-1.0))/tau*getTimeStep()*stride_;
764 4 : else height0_=kbt_/tau*getTimeStep()*stride_; // special case for gamma=1
765 : }
766 3 : else if(dampfactor_>0.0) height0_=(kbt_*dampfactor_)/tau*getTimeStep()*stride_;
767 1 : else error("TAU only makes sense in well-tempered or damped metadynamics");
768 : }
769 :
770 : // Grid Stuff
771 150 : std::vector<std::string> gmin(getNumberOfArguments());
772 296 : parseVector("GRID_MIN",gmin);
773 148 : if(gmin.size()!=getNumberOfArguments() && gmin.size()!=0) error("not enough values for GRID_MIN");
774 148 : std::vector<std::string> gmax(getNumberOfArguments());
775 296 : parseVector("GRID_MAX",gmax);
776 148 : if(gmax.size()!=getNumberOfArguments() && gmax.size()!=0) error("not enough values for GRID_MAX");
777 148 : std::vector<unsigned> gbin(getNumberOfArguments());
778 : std::vector<double> gspacing;
779 296 : parseVector("GRID_BIN",gbin);
780 148 : if(gbin.size()!=getNumberOfArguments() && gbin.size()!=0) error("not enough values for GRID_BIN");
781 296 : parseVector("GRID_SPACING",gspacing);
782 148 : if(gspacing.size()!=getNumberOfArguments() && gspacing.size()!=0) error("not enough values for GRID_SPACING");
783 148 : if(gmin.size()!=gmax.size()) error("GRID_MAX and GRID_MIN should be either present or absent");
784 148 : if(gspacing.size()!=0 && gmin.size()==0) error("If GRID_SPACING is present also GRID_MIN and GRID_MAX should be present");
785 148 : if(gbin.size()!=0 && gmin.size()==0) error("If GRID_BIN is present also GRID_MIN and GRID_MAX should be present");
786 148 : if(gmin.size()!=0) {
787 60 : if(gbin.size()==0 && gspacing.size()==0) {
788 6 : if(adaptive_==FlexibleBin::none) {
789 6 : log<<" Binsize not specified, 1/5 of sigma will be be used\n";
790 6 : plumed_assert(sigma0_.size()==getNumberOfArguments());
791 6 : gspacing.resize(getNumberOfArguments());
792 13 : for(unsigned i=0; i<gspacing.size(); i++) gspacing[i]=0.2*sigma0_[i];
793 : } else {
794 : // with adaptive hills and grid a sigma min must be specified
795 0 : for(unsigned i=0; i<sigma0min_.size(); i++) if(sigma0min_[i]<=0) error("When using ADAPTIVE Gaussians on a grid SIGMA_MIN must be specified");
796 0 : log<<" Binsize not specified, 1/5 of sigma_min will be be used\n";
797 0 : gspacing.resize(getNumberOfArguments());
798 0 : for(unsigned i=0; i<gspacing.size(); i++) gspacing[i]=0.2*sigma0min_[i];
799 : }
800 54 : } else if(gspacing.size()!=0 && gbin.size()==0) {
801 1 : log<<" The number of bins will be estimated from GRID_SPACING\n";
802 53 : } else if(gspacing.size()!=0 && gbin.size()!=0) {
803 1 : log<<" You specified both GRID_BIN and GRID_SPACING\n";
804 1 : log<<" The more conservative (highest) number of bins will be used for each variable\n";
805 : }
806 60 : if(gbin.size()==0) gbin.assign(getNumberOfArguments(),1);
807 70 : if(gspacing.size()!=0) for(unsigned i=0; i<getNumberOfArguments(); i++) {
808 : double a,b;
809 11 : Tools::convert(gmin[i],a);
810 10 : Tools::convert(gmax[i],b);
811 10 : unsigned n=((b-a)/gspacing[i])+1;
812 10 : if(gbin[i]<n) gbin[i]=n;
813 : }
814 : }
815 147 : if(gbin.size()>0) grid_=true;
816 :
817 147 : bool sparsegrid=false;
818 147 : parseFlag("GRID_SPARSE",sparsegrid);
819 147 : bool nospline=false;
820 147 : parseFlag("GRID_NOSPLINE",nospline);
821 147 : bool spline=!nospline;
822 295 : parse("GRID_WSTRIDE",wgridstride_);
823 : std::string gridfilename_;
824 147 : parse("GRID_WFILE",gridfilename_);
825 147 : parseFlag("STORE_GRIDS",storeOldGrids_);
826 147 : if(grid_ && gridfilename_.length()>0) {
827 18 : if(wgridstride_==0 ) error("frequency with which to output grid not specified use GRID_WSTRIDE");
828 : }
829 147 : if(grid_ && wgridstride_>0) {
830 18 : if(gridfilename_.length()==0) error("grid filename not specified use GRID_WFILE");
831 : }
832 :
833 : std::string gridreadfilename_;
834 147 : parse("GRID_RFILE",gridreadfilename_);
835 :
836 147 : if(!grid_&&gridfilename_.length()> 0) error("To write a grid you need first to define it!");
837 147 : if(!grid_&&gridreadfilename_.length()>0) error("To read a grid you need first to define it!");
838 :
839 : /*setup neighbor list stuff*/
840 294 : parseFlag("NLIST", nlist_);
841 147 : nlist_center_.resize(getNumberOfArguments());
842 147 : nlist_dev2_.resize(getNumberOfArguments());
843 147 : if(nlist_&&grid_) error("NLIST and GRID cannot be combined!");
844 : std::vector<double> nlist_param;
845 294 : parseVector("NLIST_PARAMETERS",nlist_param);
846 147 : if(nlist_param.size()==0)
847 : {
848 147 : nlist_param_[0]=6.0;//*DP2CUTOFF -> max distance of neighbors
849 147 : nlist_param_[1]=0.5;//*nlist_dev2_[i] -> condition for rebuilding
850 : }
851 : else
852 : {
853 0 : plumed_massert(nlist_param.size()==2,"two cutoff parameters are needed for the neighbor list");
854 0 : plumed_massert(nlist_param[0]>1.0,"the first of NLIST_PARAMETERS must be greater than 1. The smaller the first, the smaller should be the second as well");
855 0 : const double min_PARAM_1=(1.-1./std::sqrt(nlist_param[0]/2))+0.16;
856 0 : plumed_massert(nlist_param[1]>0,"the second of NLIST_PARAMETERS must be greater than 0");
857 0 : plumed_massert(nlist_param[1]<=min_PARAM_1,"the second of NLIST_PARAMETERS must be smaller to avoid systematic errors. Largest suggested value is: 1.16-1/sqrt(PARAM_0/2) = "+std::to_string(min_PARAM_1));
858 0 : nlist_param_[0]=nlist_param[0];
859 0 : nlist_param_[1]=nlist_param[1];
860 : }
861 :
862 : // Reweighting factor rct
863 147 : parseFlag("CALC_RCT",calc_rct_);
864 147 : if (calc_rct_) plumed_massert(grid_,"CALC_RCT is supported only if bias is on a grid");
865 147 : parse("RCT_USTRIDE",rct_ustride_);
866 :
867 147 : if(dampfactor_>0.0) {
868 2 : if(!grid_) error("With DAMPFACTOR you should use grids");
869 : }
870 :
871 : // Multiple walkers
872 147 : parse("WALKERS_N",mw_n_);
873 147 : parse("WALKERS_ID",mw_id_);
874 147 : if(mw_n_<=mw_id_) error("walker ID should be a numerical value less than the total number of walkers");
875 147 : parse("WALKERS_DIR",mw_dir_);
876 147 : parse("WALKERS_RSTRIDE",mw_rstride_);
877 :
878 : // MPI version
879 147 : parseFlag("WALKERS_MPI",walkers_mpi_);
880 :
881 : // Flying Gaussian
882 147 : parseFlag("FLYING_GAUSSIAN", flying_);
883 :
884 : // Inteval keyword
885 147 : std::vector<double> tmpI(2);
886 294 : parseVector("INTERVAL",tmpI);
887 147 : if(tmpI.size()!=2&&tmpI.size()!=0) error("both a lower and an upper limits must be provided with INTERVAL");
888 147 : else if(tmpI.size()==2) {
889 2 : lowI_=tmpI.at(0);
890 2 : uppI_=tmpI.at(1);
891 2 : if(getNumberOfArguments()!=1) error("INTERVAL limits correction works only for monodimensional metadynamics!");
892 2 : if(uppI_<lowI_) error("The Upper limit must be greater than the Lower limit!");
893 2 : if(getPntrToArgument(0)->isPeriodic()) error("INTERVAL cannot be used with periodic variables!");
894 2 : doInt_=true;
895 : }
896 :
897 294 : parseFlag("ACCELERATION",acceleration_);
898 : // Check for a restart acceleration if acceleration is active.
899 : std::string acc_rfilename;
900 147 : if(acceleration_) {
901 8 : parse("ACCELERATION_RFILE", acc_rfilename);
902 : }
903 :
904 147 : freq_adaptive_=false;
905 147 : parseFlag("FREQUENCY_ADAPTIVE",freq_adaptive_);
906 : //
907 147 : fa_update_frequency_=0;
908 147 : parse("FA_UPDATE_FREQUENCY",fa_update_frequency_);
909 147 : if(fa_update_frequency_!=0 && !freq_adaptive_) {
910 0 : plumed_merror("It doesn't make sense to use the FA_MAX_PACE keyword if frequency adaptive METAD hasn't been activated by using the FREQUENCY_ADAPTIVE flag");
911 : }
912 147 : if(fa_update_frequency_==0 && freq_adaptive_) {
913 0 : fa_update_frequency_=stride_;
914 : }
915 : //
916 147 : fa_max_stride_=0;
917 147 : parse("FA_MAX_PACE",fa_max_stride_);
918 147 : if(fa_max_stride_!=0 && !freq_adaptive_) {
919 0 : plumed_merror("It doesn't make sense to use the FA_MAX_PACE keyword if frequency adaptive METAD hasn't been activated by using the FREQUENCY_ADAPTIVE flag");
920 : }
921 : //
922 147 : fa_min_acceleration_=1.0;
923 147 : parse("FA_MIN_ACCELERATION",fa_min_acceleration_);
924 147 : if(fa_min_acceleration_!=1.0 && !freq_adaptive_) {
925 0 : plumed_merror("It doesn't make sense to use the FA_MIN_ACCELERATION keyword if frequency adaptive METAD hasn't been activated by using the FREQUENCY_ADAPTIVE flag");
926 : }
927 :
928 147 : checkRead();
929 :
930 147 : log.printf(" Gaussian width ");
931 147 : if (adaptive_==FlexibleBin::diffusion)log.printf(" (Note: The units of sigma are in timesteps) ");
932 147 : if (adaptive_==FlexibleBin::geometry)log.printf(" (Note: The units of sigma are in dist units) ");
933 393 : for(unsigned i=0; i<sigma0_.size(); ++i) log.printf(" %f",sigma0_[i]);
934 147 : log.printf(" Gaussian height %f\n",height0_);
935 147 : log.printf(" Gaussian deposition pace %d\n",stride_);
936 147 : log.printf(" Gaussian file %s\n",hillsfname.c_str());
937 147 : if(welltemp_) {
938 37 : log.printf(" Well-Tempered Bias Factor %f\n",biasf_);
939 37 : log.printf(" Hills relaxation time (tau) %f\n",tau);
940 37 : log.printf(" KbT %f\n",kbt_);
941 : }
942 :
943 : // Transition tempered metadynamics options
944 147 : if (tt_specs_.is_active) {
945 3 : logTemperingSpecs(tt_specs_);
946 : // Check that the appropriate transition bias quantity is calculated.
947 : // (Should never trip, given that the flag is automatically set.)
948 3 : if (!calc_transition_bias_) {
949 0 : error(" transition tempering requires calculation of a transition bias");
950 : }
951 : }
952 :
953 : // Overall tempering sanity check (this gets tricky when multiple are active).
954 : // When multiple temperings are active, it's fine to have one tempering attempt
955 : // to increase hill size with increasing bias, so long as the others can shrink
956 : // the hills faster than it increases their size in the long-time limit.
957 : // This set of checks ensures that the hill sizes eventually decay to zero as c(t)
958 : // diverges to infinity.
959 : // The alpha parameter allows hills to decay as 1/t^alpha instead of 1/t,
960 : // a slower decay, so as t -> infinity, only the temperings with the largest
961 : // alphas govern the final asymptotic decay. (Alpha helps prevent false convergence.)
962 147 : if (welltemp_ || dampfactor_ > 0.0 || tt_specs_.is_active) {
963 : // Determine the number of active temperings.
964 : int n_active = 0;
965 42 : if (welltemp_) n_active++;
966 42 : if (dampfactor_ > 0.0) n_active++;
967 42 : if (tt_specs_.is_active) n_active++;
968 : // Find the greatest alpha.
969 42 : double greatest_alpha = 0.0;
970 42 : if (welltemp_) greatest_alpha = std::max(greatest_alpha, 1.0);
971 44 : if (dampfactor_ > 0.0) greatest_alpha = std::max(greatest_alpha, 1.0);
972 45 : if (tt_specs_.is_active) greatest_alpha = std::max(greatest_alpha, tt_specs_.alpha);
973 : // Find the least alpha.
974 42 : double least_alpha = 1.0;
975 : if (welltemp_) least_alpha = std::min(least_alpha, 1.0);
976 42 : if (dampfactor_ > 0.0) least_alpha = std::min(least_alpha, 1.0);
977 43 : if (tt_specs_.is_active) least_alpha = std::min(least_alpha, tt_specs_.alpha);
978 : // Find the inverse harmonic average of the delta T parameters for all
979 : // of the temperings with the greatest alpha values.
980 : double total_governing_deltaT_inv = 0.0;
981 42 : if (welltemp_ && 1.0 == greatest_alpha && biasf_ != 1.0) total_governing_deltaT_inv += 1.0 / (biasf_ - 1.0);
982 42 : if (dampfactor_ > 0.0 && 1.0 == greatest_alpha) total_governing_deltaT_inv += 1.0 / (dampfactor_);
983 42 : if (tt_specs_.is_active && tt_specs_.alpha == greatest_alpha) total_governing_deltaT_inv += 1.0 / (tt_specs_.biasf - 1.0);
984 : // Give a newbie-friendly error message for people using one tempering if
985 : // only one is active.
986 42 : if (n_active == 1 && total_governing_deltaT_inv < 0.0) {
987 0 : error("for stable tempering, the bias factor must be greater than one");
988 : // Give a slightly more complex error message to users stacking multiple
989 : // tempering options at a time, but all with uniform alpha values.
990 42 : } else if (total_governing_deltaT_inv < 0.0 && greatest_alpha == least_alpha) {
991 0 : error("for stable tempering, the sum of the inverse Delta T parameters must be greater than zero!");
992 : // Give the most technical error message to users stacking multiple tempering
993 : // options with different alpha parameters.
994 42 : } else if (total_governing_deltaT_inv < 0.0 && greatest_alpha != least_alpha) {
995 0 : error("for stable tempering, the sum of the inverse Delta T parameters for the greatest asymptotic hill decay exponents must be greater than zero!");
996 : }
997 : }
998 :
999 147 : if(doInt_) log.printf(" Upper and Lower limits boundaries for the bias are activated at %f - %f\n", lowI_, uppI_);
1000 :
1001 147 : if(grid_) {
1002 59 : log.printf(" Grid min");
1003 158 : for(unsigned i=0; i<gmin.size(); ++i) log.printf(" %s",gmin[i].c_str() );
1004 59 : log.printf("\n");
1005 59 : log.printf(" Grid max");
1006 158 : for(unsigned i=0; i<gmax.size(); ++i) log.printf(" %s",gmax[i].c_str() );
1007 59 : log.printf("\n");
1008 59 : log.printf(" Grid bin");
1009 158 : for(unsigned i=0; i<gbin.size(); ++i) log.printf(" %u",gbin[i]);
1010 59 : log.printf("\n");
1011 59 : if(spline) {log.printf(" Grid uses spline interpolation\n");}
1012 59 : if(sparsegrid) {log.printf(" Grid uses sparse grid\n");}
1013 59 : if(wgridstride_>0) {log.printf(" Grid is written on file %s with stride %d\n",gridfilename_.c_str(),wgridstride_);}
1014 : }
1015 :
1016 147 : if(mw_n_>1) {
1017 6 : if(walkers_mpi_) error("MPI version of multiple walkers is not compatible with filesystem version of multiple walkers");
1018 6 : log.printf(" %d multiple walkers active\n",mw_n_);
1019 6 : log.printf(" walker id %d\n",mw_id_);
1020 6 : log.printf(" reading stride %d\n",mw_rstride_);
1021 6 : if(mw_dir_!="")log.printf(" directory with hills files %s\n",mw_dir_.c_str());
1022 : } else {
1023 141 : if(walkers_mpi_) {
1024 38 : log.printf(" Multiple walkers active using MPI communnication\n");
1025 38 : if(mw_dir_!="")log.printf(" directory with hills files %s\n",mw_dir_.c_str());
1026 38 : if(comm.Get_rank()==0) {
1027 : // Only root of group can communicate with other walkers
1028 23 : mpi_nw_=multi_sim_comm.Get_size();
1029 : }
1030 : // Communicate to the other members of the same group
1031 : // info abount number of walkers and walker index
1032 38 : comm.Bcast(mpi_nw_,0);
1033 : }
1034 : }
1035 :
1036 147 : if(flying_) {
1037 6 : if(!walkers_mpi_) error("Flying Gaussian method must be used with MPI version of multiple walkers");
1038 6 : log.printf(" Flying Gaussian method with %d walkers active\n",mpi_nw_);
1039 : }
1040 :
1041 147 : if(nlist_) {
1042 1 : addComponent("nlker");
1043 1 : componentIsNotPeriodic("nlker");
1044 1 : addComponent("nlsteps");
1045 2 : componentIsNotPeriodic("nlsteps");
1046 : }
1047 :
1048 147 : if(calc_rct_) {
1049 12 : addComponent("rbias"); componentIsNotPeriodic("rbias");
1050 12 : addComponent("rct"); componentIsNotPeriodic("rct");
1051 6 : log.printf(" The c(t) reweighting factor will be calculated every %u hills\n",rct_ustride_);
1052 12 : getPntrToComponent("rct")->set(reweight_factor_);
1053 : }
1054 :
1055 149 : if(calc_work_) { addComponent("work"); componentIsNotPeriodic("work"); }
1056 :
1057 147 : if(acceleration_) {
1058 4 : if (kbt_ == 0.0) {
1059 0 : error("The calculation of the acceleration works only if simulation temperature has been defined");
1060 : }
1061 4 : log.printf(" calculation on the fly of the acceleration factor\n");
1062 12 : addComponent("acc"); componentIsNotPeriodic("acc");
1063 : // Set the initial value of the the acceleration.
1064 : // If this is not a restart, set to 1.0.
1065 4 : if (acc_rfilename.length() == 0) {
1066 4 : getPntrToComponent("acc")->set(1.0);
1067 2 : if(getRestart()) {
1068 1 : log.printf(" WARNING: calculating the acceleration factor in a restarted run without reading in the previous value will most likely lead to incorrect results.\n");
1069 1 : log.printf(" You should use the ACCELERATION_RFILE keyword.\n");
1070 : }
1071 : // Otherwise, read and set the restart value.
1072 : } else {
1073 : // Restart of acceleration does not make sense if the restart timestep is zero.
1074 : //if (getStep() == 0) {
1075 : // error("Restarting calculation of acceleration factors works only if simulation timestep is restarted correctly");
1076 : //}
1077 : // Open the ACCELERATION_RFILE.
1078 2 : IFile acc_rfile;
1079 2 : acc_rfile.link(*this);
1080 2 : if(acc_rfile.FileExist(acc_rfilename)) {
1081 2 : acc_rfile.open(acc_rfilename);
1082 : } else {
1083 0 : error("The ACCELERATION_RFILE file you want to read: " + acc_rfilename + ", cannot be found!");
1084 : }
1085 : // Read the file to find the restart acceleration.
1086 2 : double acc_rmean=0.0;
1087 2 : double acc_rtime=0.0;
1088 : bool found=false;
1089 2 : std::string acclabel = getLabel() + ".acc";
1090 2 : acc_rfile.allowIgnoredFields();
1091 248 : while(acc_rfile.scanField("time", acc_rtime)) {
1092 122 : acc_rfile.scanField(acclabel, acc_rmean);
1093 122 : acc_rfile.scanField();
1094 : found=true;
1095 : }
1096 2 : if(!found) error("The ACCELERATION_RFILE file you want to read: " + acc_rfilename + ", does not contain a time field!");
1097 2 : acc_restart_mean_ = acc_rmean;
1098 : // Set component based on the read values.
1099 2 : getPntrToComponent("acc")->set(acc_rmean);
1100 2 : log.printf(" initial acceleration factor read from file %s: value of %f at time %f\n",acc_rfilename.c_str(),acc_rmean,acc_rtime);
1101 2 : }
1102 : }
1103 :
1104 147 : if (calc_max_bias_) {
1105 0 : if (!grid_) error("Calculating the maximum bias on the fly works only with a grid");
1106 0 : log.printf(" calculation on the fly of the maximum bias max(V(s,t)) \n");
1107 0 : addComponent("maxbias");
1108 0 : componentIsNotPeriodic("maxbias");
1109 : }
1110 :
1111 147 : if (calc_transition_bias_) {
1112 13 : if (!grid_) error("Calculating the transition bias on the fly works only with a grid");
1113 13 : log.printf(" calculation on the fly of the transition bias V*(t)\n");
1114 13 : addComponent("transbias");
1115 13 : componentIsNotPeriodic("transbias");
1116 13 : log<<" Number of transition wells "<<transitionwells_.size()<<"\n";
1117 13 : if (transitionwells_.size() == 0) error("Calculating the transition bias on the fly requires definition of at least one transition well");
1118 : // Check that a grid is in use.
1119 13 : if (!grid_) error(" transition barrier finding requires a grid for the bias");
1120 : // Log the wells and check that they are in the grid.
1121 39 : for (unsigned i = 0; i < transitionwells_.size(); i++) {
1122 : // Log the coordinate.
1123 26 : log.printf(" Transition well %d at coordinate ", i);
1124 64 : for (unsigned j = 0; j < getNumberOfArguments(); j++) log.printf("%f ", transitionwells_[i][j]);
1125 26 : log.printf("\n");
1126 : // Check that the coordinate is in the grid.
1127 64 : for (unsigned j = 0; j < getNumberOfArguments(); j++) {
1128 : double max, min;
1129 38 : Tools::convert(gmin[j], min);
1130 38 : Tools::convert(gmax[j], max);
1131 38 : if (transitionwells_[i][j] < min || transitionwells_[i][j] > max) error(" transition well is not in grid");
1132 : }
1133 : }
1134 : }
1135 :
1136 147 : if(freq_adaptive_) {
1137 2 : if(!acceleration_) {
1138 0 : plumed_merror("Frequency adaptive metadynamics only works if the calculation of the acceleration factor is enabled with the ACCELERATION keyword\n");
1139 : }
1140 2 : if(walkers_mpi_) {
1141 0 : plumed_merror("Combining frequency adaptive metadynamics with MPI multiple walkers is not allowed");
1142 : }
1143 :
1144 2 : log.printf(" Frequency adaptive metadynamics enabled\n");
1145 2 : if(getRestart() && acc_rfilename.length() == 0) {
1146 0 : log.printf(" WARNING: using the frequency adaptive scheme in a restarted run without reading in the previous value of the acceleration factor will most likely lead to incorrect results.\n");
1147 0 : log.printf(" You should use the ACCELERATION_RFILE keyword.\n");
1148 : }
1149 2 : log.printf(" The frequency for hill addition will change dynamically based on the metadynamics acceleration factor\n");
1150 2 : log.printf(" The hill addition frequency will be updated every %d steps\n",fa_update_frequency_);
1151 2 : if(fa_min_acceleration_>1.0) {
1152 2 : log.printf(" The hill addition frequency will only be updated once the metadynamics acceleration factor becomes larger than %.1f \n",fa_min_acceleration_);
1153 : }
1154 2 : if(fa_max_stride_!=0) {
1155 2 : log.printf(" The hill addition frequency will not become larger than %d steps\n",fa_max_stride_);
1156 : }
1157 4 : addComponent("pace"); componentIsNotPeriodic("pace");
1158 2 : updateFrequencyAdaptiveStride();
1159 : }
1160 :
1161 : // initializing and checking grid
1162 : bool restartedFromGrid=false; // restart from grid file
1163 147 : if(grid_) {
1164 59 : if(!(gridreadfilename_.length()>0)) {
1165 : // check for mesh and sigma size
1166 113 : for(unsigned i=0; i<getNumberOfArguments(); i++) {
1167 : double a,b;
1168 72 : Tools::convert(gmin[i],a);
1169 72 : Tools::convert(gmax[i],b);
1170 72 : double mesh=(b-a)/((double)gbin[i]);
1171 72 : if(adaptive_==FlexibleBin::none) {
1172 72 : if(mesh>0.5*sigma0_[i]) log<<" WARNING: Using a METAD with a Grid Spacing larger than half of the Gaussians width (SIGMA) can produce artifacts\n";
1173 : } else {
1174 0 : if(sigma0min_[i]<0.) error("When using ADAPTIVE Gaussians on a grid SIGMA_MIN must be specified");
1175 0 : if(mesh>0.5*sigma0min_[i]) log<<" WARNING: to use a METAD with a GRID and ADAPTIVE you need to set a Grid Spacing lower than half of the Gaussians (SIGMA_MIN) \n";
1176 : }
1177 : }
1178 41 : std::string funcl=getLabel() + ".bias";
1179 41 : if(!sparsegrid) {BiasGrid_=Tools::make_unique<Grid>(funcl,getArguments(),gmin,gmax,gbin,spline,true);}
1180 6 : else {BiasGrid_=Tools::make_unique<SparseGrid>(funcl,getArguments(),gmin,gmax,gbin,spline,true);}
1181 41 : std::vector<std::string> actualmin=BiasGrid_->getMin();
1182 41 : std::vector<std::string> actualmax=BiasGrid_->getMax();
1183 113 : for(unsigned i=0; i<getNumberOfArguments(); i++) {
1184 : std::string is;
1185 72 : Tools::convert(i,is);
1186 72 : if(gmin[i]!=actualmin[i]) error("GRID_MIN["+is+"] must be adjusted to "+actualmin[i]+" to fit periodicity");
1187 72 : if(gmax[i]!=actualmax[i]) error("GRID_MAX["+is+"] must be adjusted to "+actualmax[i]+" to fit periodicity");
1188 : }
1189 41 : } else {
1190 : // read the grid in input, find the keys
1191 : #ifdef __PLUMED_HAS_GETCWD
1192 18 : if(walkers_mpi_&&gridreadfilename_.at(0)!='/') {
1193 : //if possible the root replica will share its current folder so that all walkers will read the same file
1194 0 : char cwd[4096]= {0};
1195 : const char* ret=getcwd(cwd,4096);
1196 0 : plumed_assert(ret)<<"Name of current directory too long, increase buffer size";
1197 0 : gridreadfilename_ = "/" + gridreadfilename_;
1198 0 : gridreadfilename_ = ret + gridreadfilename_;
1199 0 : if(comm.Get_rank()==0) multi_sim_comm.Bcast(gridreadfilename_,0);
1200 0 : comm.Bcast(gridreadfilename_,0);
1201 : }
1202 : #endif
1203 18 : IFile gridfile;
1204 18 : gridfile.link(*this);
1205 18 : if(gridfile.FileExist(gridreadfilename_)) {
1206 18 : gridfile.open(gridreadfilename_);
1207 : } else {
1208 0 : error("The GRID file you want to read: " + gridreadfilename_ + ", cannot be found!");
1209 : }
1210 18 : std::string funcl=getLabel() + ".bias";
1211 36 : BiasGrid_=GridBase::create(funcl, getArguments(), gridfile, gmin, gmax, gbin, sparsegrid, spline, true);
1212 18 : if(BiasGrid_->getDimension()!=getNumberOfArguments()) error("mismatch between dimensionality of input grid and number of arguments");
1213 45 : for(unsigned i=0; i<getNumberOfArguments(); ++i) {
1214 54 : if( getPntrToArgument(i)->isPeriodic()!=BiasGrid_->getIsPeriodic()[i] ) error("periodicity mismatch between arguments and input bias");
1215 : double a, b;
1216 27 : Tools::convert(gmin[i],a);
1217 27 : Tools::convert(gmax[i],b);
1218 27 : double mesh=(b-a)/((double)gbin[i]);
1219 27 : if(mesh>0.5*sigma0_[i]) log<<" WARNING: Using a METAD with a Grid Spacing larger than half of the Gaussians width can produce artifacts\n";
1220 : }
1221 18 : log.printf(" Restarting from %s\n",gridreadfilename_.c_str());
1222 18 : if(getRestart()) restartedFromGrid=true;
1223 18 : }
1224 : }
1225 :
1226 : // if we are restarting from GRID and using WALKERS_MPI we can check that all walkers have actually read the grid
1227 147 : if(getRestart()&&walkers_mpi_) {
1228 9 : std::vector<int> restarted(mpi_nw_,0);
1229 9 : if(comm.Get_rank()==0) multi_sim_comm.Allgather(int(restartedFromGrid), restarted);
1230 9 : comm.Bcast(restarted,0);
1231 : int result = std::accumulate(restarted.begin(),restarted.end(),0);
1232 9 : if(result!=0&&result!=mpi_nw_) error("in this WALKERS_MPI run some replica have restarted from GRID while other do not!");
1233 : }
1234 :
1235 : #ifdef __PLUMED_HAS_GETCWD
1236 185 : if(walkers_mpi_&&mw_dir_==""&&hillsfname.at(0)!='/') {
1237 : //if possible the root replica will share its current folder so that all walkers will read the same file
1238 38 : char cwd[4096]= {0};
1239 : const char* ret=getcwd(cwd,4096);
1240 38 : plumed_assert(ret)<<"Name of current directory too long, increase buffer size";
1241 : mw_dir_ = ret;
1242 38 : mw_dir_ = mw_dir_ + "/";
1243 38 : if(comm.Get_rank()==0) multi_sim_comm.Bcast(mw_dir_,0);
1244 38 : comm.Bcast(mw_dir_,0);
1245 : }
1246 : #endif
1247 :
1248 : // creating std::vector of ifile* for hills reading
1249 : // open all files at the beginning and read Gaussians if restarting
1250 : bool restartedFromHills=false; // restart from hills files
1251 306 : for(int i=0; i<mw_n_; ++i) {
1252 : std::string fname;
1253 159 : if(mw_dir_!="") {
1254 47 : if(mw_n_>1) {
1255 9 : std::stringstream out; out << i;
1256 18 : fname = mw_dir_+"/"+hillsfname+"."+out.str();
1257 47 : } else if(walkers_mpi_) {
1258 76 : fname = mw_dir_+"/"+hillsfname;
1259 : } else {
1260 : fname = hillsfname;
1261 : }
1262 : } else {
1263 112 : if(mw_n_>1) {
1264 9 : std::stringstream out; out << i;
1265 18 : fname = hillsfname+"."+out.str();
1266 9 : } else {
1267 : fname = hillsfname;
1268 : }
1269 : }
1270 159 : ifiles_.emplace_back(Tools::make_unique<IFile>());
1271 : // this is just a shortcut pointer to the last element:
1272 : IFile *ifile = ifiles_.back().get();
1273 159 : ifilesnames_.push_back(fname);
1274 159 : ifile->link(*this);
1275 159 : if(ifile->FileExist(fname)) {
1276 35 : ifile->open(fname);
1277 35 : if(getRestart()&&!restartedFromGrid) {
1278 19 : log.printf(" Restarting from %s:",ifilesnames_[i].c_str());
1279 19 : readGaussians(ifiles_[i].get());
1280 : restartedFromHills=true;
1281 : }
1282 35 : ifiles_[i]->reset(false);
1283 : // close only the walker own hills file for later writing
1284 35 : if(i==mw_id_) ifiles_[i]->close();
1285 : } else {
1286 : // in case a file does not exist and we are restarting, complain that the file was not found
1287 124 : if(getRestart()&&!restartedFromGrid) error("restart file "+fname+" not found");
1288 : }
1289 : }
1290 :
1291 : // if we are restarting from FILE and using WALKERS_MPI we can check that all walkers have actually read the FILE
1292 147 : if(getRestart()&&walkers_mpi_) {
1293 9 : std::vector<int> restarted(mpi_nw_,0);
1294 9 : if(comm.Get_rank()==0) multi_sim_comm.Allgather(int(restartedFromHills), restarted);
1295 9 : comm.Bcast(restarted,0);
1296 : int result = std::accumulate(restarted.begin(),restarted.end(),0);
1297 9 : if(result!=0&&result!=mpi_nw_) error("in this WALKERS_MPI run some replica have restarted from FILE while other do not!");
1298 : }
1299 :
1300 147 : comm.Barrier();
1301 : // this barrier is needed when using walkers_mpi
1302 : // to be sure that all files have been read before
1303 : // backing them up
1304 : // it should not be used when walkers_mpi is false otherwise
1305 : // it would introduce troubles when using replicas without METAD
1306 : // (e.g. in bias exchange with a neutral replica)
1307 : // see issue #168 on github
1308 147 : if(comm.Get_rank()==0 && walkers_mpi_) multi_sim_comm.Barrier();
1309 :
1310 147 : if(targetfilename_.length()>0) {
1311 2 : IFile gridfile; gridfile.open(targetfilename_);
1312 2 : std::string funcl=getLabel() + ".target";
1313 4 : TargetGrid_=GridBase::create(funcl,getArguments(),gridfile,false,false,true);
1314 2 : if(TargetGrid_->getDimension()!=getNumberOfArguments()) error("mismatch between dimensionality of input grid and number of arguments");
1315 4 : for(unsigned i=0; i<getNumberOfArguments(); ++i) {
1316 4 : if( getPntrToArgument(i)->isPeriodic()!=TargetGrid_->getIsPeriodic()[i] ) error("periodicity mismatch between arguments and input bias");
1317 : }
1318 2 : }
1319 :
1320 147 : if(getRestart()) {
1321 : // if this is a restart the neighbor list should be immediately updated
1322 37 : if(nlist_) nlist_update_=true;
1323 : // Calculate the Tiwary-Parrinello reweighting factor if we are restarting from previous hills
1324 37 : if(calc_rct_) computeReweightingFactor();
1325 : // Calculate all special bias quantities desired if restarting with nonzero bias.
1326 37 : if(calc_max_bias_) {
1327 0 : max_bias_ = BiasGrid_->getMaxValue();
1328 0 : getPntrToComponent("maxbias")->set(max_bias_);
1329 : }
1330 37 : if(calc_transition_bias_) {
1331 13 : transition_bias_ = getTransitionBarrierBias();
1332 26 : getPntrToComponent("transbias")->set(transition_bias_);
1333 : }
1334 : }
1335 :
1336 : // open grid file for writing
1337 147 : if(wgridstride_>0) {
1338 18 : gridfile_.link(*this);
1339 18 : if(walkers_mpi_) {
1340 0 : int r=0;
1341 0 : if(comm.Get_rank()==0) r=multi_sim_comm.Get_rank();
1342 0 : comm.Bcast(r,0);
1343 0 : if(r>0) gridfilename_="/dev/null";
1344 0 : gridfile_.enforceSuffix("");
1345 : }
1346 18 : if(mw_n_>1) gridfile_.enforceSuffix("");
1347 18 : gridfile_.open(gridfilename_);
1348 : }
1349 :
1350 : // open hills file for writing
1351 147 : hillsOfile_.link(*this);
1352 147 : if(walkers_mpi_) {
1353 38 : int r=0;
1354 38 : if(comm.Get_rank()==0) r=multi_sim_comm.Get_rank();
1355 38 : comm.Bcast(r,0);
1356 38 : if(r>0) ifilesnames_[mw_id_]="/dev/null";
1357 76 : hillsOfile_.enforceSuffix("");
1358 : }
1359 153 : if(mw_n_>1) hillsOfile_.enforceSuffix("");
1360 147 : hillsOfile_.open(ifilesnames_[mw_id_]);
1361 147 : if(fmt_.length()>0) hillsOfile_.fmtField(fmt_);
1362 147 : hillsOfile_.addConstantField("multivariate");
1363 147 : hillsOfile_.addConstantField("kerneltype");
1364 147 : if(doInt_) {
1365 4 : hillsOfile_.addConstantField("lower_int").printField("lower_int",lowI_);
1366 4 : hillsOfile_.addConstantField("upper_int").printField("upper_int",uppI_);
1367 : }
1368 : hillsOfile_.setHeavyFlush();
1369 : // output periodicities of variables
1370 412 : for(unsigned i=0; i<getNumberOfArguments(); ++i) hillsOfile_.setupPrintValue( getPntrToArgument(i) );
1371 :
1372 : bool concurrent=false;
1373 147 : const ActionSet&actionSet(plumed.getActionSet());
1374 538 : for(const auto & p : actionSet) if(dynamic_cast<MetaD*>(p.get())) { concurrent=true; break; }
1375 147 : if(concurrent) log<<" You are using concurrent metadynamics\n";
1376 147 : if(rect_biasf_.size()>0) {
1377 18 : if(walkers_mpi_) {
1378 12 : log<<" You are using RECT in its 'altruistic' implementation\n";
1379 : }{
1380 18 : log<<" You are using RECT\n";
1381 : }
1382 : }
1383 :
1384 294 : log<<" Bibliography "<<plumed.cite("Laio and Parrinello, PNAS 99, 12562 (2002)");
1385 221 : if(welltemp_) log<<plumed.cite("Barducci, Bussi, and Parrinello, Phys. Rev. Lett. 100, 020603 (2008)");
1386 147 : if(tt_specs_.is_active) {
1387 6 : log << plumed.cite("Dama, Rotskoff, Parrinello, and Voth, J. Chem. Theory Comput. 10, 3626 (2014)");
1388 9 : log << plumed.cite("Dama, Parrinello, and Voth, Phys. Rev. Lett. 112, 240602 (2014)");
1389 : }
1390 235 : if(mw_n_>1||walkers_mpi_) log<<plumed.cite("Raiteri, Laio, Gervasio, Micheletti, and Parrinello, J. Phys. Chem. B 110, 3533 (2006)");
1391 189 : if(adaptive_!=FlexibleBin::none) log<<plumed.cite("Branduardi, Bussi, and Parrinello, J. Chem. Theory Comput. 8, 2247 (2012)");
1392 151 : if(doInt_) log<<plumed.cite("Baftizadeh, Cossio, Pietrucci, and Laio, Curr. Phys. Chem. 2, 79 (2012)");
1393 155 : if(acceleration_) log<<plumed.cite("Pratyush and Parrinello, Phys. Rev. Lett. 111, 230602 (2013)");
1394 159 : if(calc_rct_) log<<plumed.cite("Pratyush and Parrinello, J. Phys. Chem. B, 119, 736 (2015)");
1395 305 : if(concurrent || rect_biasf_.size()>0) log<<plumed.cite("Gil-Ley and Bussi, J. Chem. Theory Comput. 11, 1077 (2015)");
1396 171 : if(rect_biasf_.size()>0 && walkers_mpi_) log<<plumed.cite("Hosek, Toulcova, Bortolato, and Spiwok, J. Phys. Chem. B 120, 2209 (2016)");
1397 147 : if(targetfilename_.length()>0) {
1398 4 : log<<plumed.cite("White, Dama, and Voth, J. Chem. Theory Comput. 11, 2451 (2015)");
1399 4 : log<<plumed.cite("Marinelli and Faraldo-Gómez, Biophys. J. 108, 2779 (2015)");
1400 6 : log<<plumed.cite("Gil-Ley, Bottaro, and Bussi, J. Chem. Theory Comput. 12, 2790 (2016)");
1401 : }
1402 151 : if(freq_adaptive_) log<<plumed.cite("Wang, Valsson, Tiwary, Parrinello, and Lindorff-Larsen, J. Chem. Phys. 149, 072309 (2018)");
1403 147 : log<<"\n";
1404 387 : }
1405 :
1406 149 : void MetaD::readTemperingSpecs(TemperingSpecs &t_specs)
1407 : {
1408 : // Set global tempering parameters.
1409 149 : parse(t_specs.name_stem + "BIASFACTOR", t_specs.biasf);
1410 149 : if (t_specs.biasf != -1.0) {
1411 3 : if (kbt_ == 0.0) {
1412 0 : error("Unless the MD engine passes the temperature to plumed, with tempered metad you must specify it using TEMP");
1413 : }
1414 3 : if (t_specs.biasf == 1.0) {
1415 0 : error("A bias factor of 1 corresponds to zero delta T and zero hill size, so it is not allowed.");
1416 : }
1417 3 : t_specs.is_active = true;
1418 3 : parse(t_specs.name_stem + "BIASTHRESHOLD", t_specs.threshold);
1419 3 : if (t_specs.threshold < 0.0) {
1420 0 : error(t_specs.name + " bias threshold is nonsensical");
1421 : }
1422 3 : parse(t_specs.name_stem + "ALPHA", t_specs.alpha);
1423 3 : if (t_specs.alpha <= 0.0 || t_specs.alpha > 1.0) {
1424 0 : error(t_specs.name + " decay shape parameter alpha is nonsensical");
1425 : }
1426 : }
1427 149 : }
1428 :
1429 3 : void MetaD::logTemperingSpecs(const TemperingSpecs &t_specs)
1430 : {
1431 3 : log.printf(" %s bias factor %f\n", t_specs.name.c_str(), t_specs.biasf);
1432 3 : log.printf(" KbT %f\n", kbt_);
1433 3 : if (t_specs.threshold != 0.0) log.printf(" %s bias threshold %f\n", t_specs.name.c_str(), t_specs.threshold);
1434 3 : if (t_specs.alpha != 1.0) log.printf(" %s decay shape parameter alpha %f\n", t_specs.name.c_str(), t_specs.alpha);
1435 3 : }
1436 :
1437 6036 : void MetaD::readGaussians(IFile *ifile)
1438 : {
1439 6036 : unsigned ncv=getNumberOfArguments();
1440 6036 : std::vector<double> center(ncv);
1441 6036 : std::vector<double> sigma(ncv);
1442 : double height;
1443 : int nhills=0;
1444 6036 : bool multivariate=false;
1445 :
1446 : std::vector<Value> tmpvalues;
1447 18121 : for(unsigned j=0; j<getNumberOfArguments(); ++j) tmpvalues.push_back( Value( this, getPntrToArgument(j)->getName(), false ) );
1448 :
1449 8156 : while(scanOneHill(ifile,tmpvalues,center,sigma,height,multivariate))
1450 : {
1451 2120 : nhills++;
1452 : // note that for gamma=1 we store directly -F
1453 2120 : if(welltemp_ && biasf_>1.0) height*=(biasf_-1.0)/biasf_;
1454 2120 : addGaussian(Gaussian(multivariate,height,center,sigma));
1455 : }
1456 6036 : log.printf(" %d Gaussians read\n",nhills);
1457 12072 : }
1458 :
1459 2918 : void MetaD::writeGaussian(const Gaussian& hill, OFile&file)
1460 : {
1461 2918 : unsigned ncv=getNumberOfArguments();
1462 2918 : file.printField("time",getTimeStep()*getStep());
1463 8182 : for(unsigned i=0; i<ncv; ++i) {
1464 5264 : file.printField(getPntrToArgument(i),hill.center[i]);
1465 : }
1466 5836 : hillsOfile_.printField("kerneltype","stretched-gaussian");
1467 2918 : if(hill.multivariate) {
1468 892 : hillsOfile_.printField("multivariate","true");
1469 : Matrix<double> mymatrix(ncv,ncv);
1470 : unsigned k=0;
1471 1047 : for(unsigned i=0; i<ncv; i++) {
1472 1357 : for(unsigned j=i; j<ncv; j++) {
1473 : // recompose the full inverse matrix
1474 756 : mymatrix(i,j)=mymatrix(j,i)=hill.sigma[k];
1475 756 : k++;
1476 : }
1477 : }
1478 : // invert it
1479 : Matrix<double> invmatrix(ncv,ncv);
1480 446 : Invert(mymatrix,invmatrix);
1481 : // enforce symmetry
1482 1047 : for(unsigned i=0; i<ncv; i++) {
1483 1357 : for(unsigned j=i; j<ncv; j++) {
1484 756 : invmatrix(i,j)=invmatrix(j,i);
1485 : }
1486 : }
1487 :
1488 : // do cholesky so to have a "sigma like" number
1489 : Matrix<double> lower(ncv,ncv);
1490 446 : cholesky(invmatrix,lower);
1491 : // loop in band form
1492 1047 : for(unsigned i=0; i<ncv; i++) {
1493 1357 : for(unsigned j=0; j<ncv-i; j++) {
1494 1512 : file.printField("sigma_"+getPntrToArgument(j+i)->getName()+"_"+getPntrToArgument(j)->getName(),lower(j+i,j));
1495 : }
1496 : }
1497 : } else {
1498 4944 : hillsOfile_.printField("multivariate","false");
1499 7135 : for(unsigned i=0; i<ncv; ++i)
1500 9326 : file.printField("sigma_"+getPntrToArgument(i)->getName(),hill.sigma[i]);
1501 : }
1502 2918 : double height=hill.height;
1503 : // note that for gamma=1 we store directly -F
1504 2918 : if(welltemp_ && biasf_>1.0) height*=biasf_/(biasf_-1.0);
1505 5836 : file.printField("height",height).printField("biasf",biasf_);
1506 4427 : if(mw_n_>1) file.printField("clock",int(std::time(0)));
1507 2918 : file.printField();
1508 2918 : }
1509 :
1510 5200 : void MetaD::addGaussian(const Gaussian& hill)
1511 : {
1512 5200 : if(grid_) {
1513 636 : size_t ncv=getNumberOfArguments();
1514 636 : std::vector<unsigned> nneighb=getGaussianSupport(hill);
1515 636 : std::vector<Grid::index_t> neighbors=BiasGrid_->getNeighbors(hill.center,nneighb);
1516 636 : std::vector<double> der(ncv);
1517 636 : std::vector<double> xx(ncv);
1518 636 : if(comm.Get_size()==1) {
1519 : // for performance reasons and thread safety
1520 540 : std::vector<double> dp(ncv);
1521 55188 : for(size_t i=0; i<neighbors.size(); ++i) {
1522 54648 : Grid::index_t ineigh=neighbors[i];
1523 158526 : for(unsigned j=0; j<ncv; ++j) der[j]=0.0;
1524 54648 : BiasGrid_->getPoint(ineigh,xx);
1525 54648 : double bias=evaluateGaussianAndDerivatives(xx,hill,der,dp);
1526 54648 : BiasGrid_->addValueAndDerivatives(ineigh,bias,der);
1527 : }
1528 : } else {
1529 96 : unsigned stride=comm.Get_size();
1530 96 : unsigned rank=comm.Get_rank();
1531 96 : std::vector<double> allder(ncv*neighbors.size(),0.0);
1532 96 : std::vector<double> n_der(ncv,0.0);
1533 96 : std::vector<double> allbias(neighbors.size(),0.0);
1534 : // for performance reasons and thread safety
1535 96 : std::vector<double> dp(ncv);
1536 27148 : for(unsigned i=rank; i<neighbors.size(); i+=stride) {
1537 27052 : Grid::index_t ineigh=neighbors[i];
1538 81156 : for(unsigned j=0; j<ncv; ++j) n_der[j]=0.0;
1539 27052 : BiasGrid_->getPoint(ineigh,xx);
1540 27052 : allbias[i]=evaluateGaussianAndDerivatives(xx,hill,n_der,dp);
1541 81156 : for(unsigned j=0; j<ncv; j++) allder[ncv*i+j]=n_der[j];
1542 : }
1543 96 : comm.Sum(allbias);
1544 96 : comm.Sum(allder);
1545 103200 : for(unsigned i=0; i<neighbors.size(); ++i) {
1546 103104 : Grid::index_t ineigh=neighbors[i];
1547 309312 : for(unsigned j=0; j<ncv; ++j) der[j]=allder[ncv*i+j];
1548 103104 : BiasGrid_->addValueAndDerivatives(ineigh,allbias[i],der);
1549 : }
1550 : }
1551 4564 : } else hills_.push_back(hill);
1552 5200 : }
1553 :
1554 636 : std::vector<unsigned> MetaD::getGaussianSupport(const Gaussian& hill)
1555 : {
1556 : std::vector<unsigned> nneigh;
1557 : std::vector<double> cutoff;
1558 636 : unsigned ncv=getNumberOfArguments();
1559 :
1560 : // traditional or flexible hill?
1561 636 : if(hill.multivariate) {
1562 : unsigned k=0;
1563 : Matrix<double> mymatrix(ncv,ncv);
1564 0 : for(unsigned i=0; i<ncv; i++) {
1565 0 : for(unsigned j=i; j<ncv; j++) {
1566 : // recompose the full inverse matrix
1567 0 : mymatrix(i,j)=mymatrix(j,i)=hill.sigma[k];
1568 0 : k++;
1569 : }
1570 : }
1571 : // Reinvert so to have the ellipses
1572 : Matrix<double> myinv(ncv,ncv);
1573 0 : Invert(mymatrix,myinv);
1574 : Matrix<double> myautovec(ncv,ncv);
1575 0 : std::vector<double> myautoval(ncv); //should I take this or their square root?
1576 0 : diagMat(myinv,myautoval,myautovec);
1577 : double maxautoval=0.;
1578 : unsigned ind_maxautoval; ind_maxautoval=ncv;
1579 0 : for(unsigned i=0; i<ncv; i++) {
1580 0 : if(myautoval[i]>maxautoval) {maxautoval=myautoval[i]; ind_maxautoval=i;}
1581 : }
1582 0 : for(unsigned i=0; i<ncv; i++) {
1583 0 : cutoff.push_back(std::sqrt(2.0*dp2cutoff)*std::abs(std::sqrt(maxautoval)*myautovec(i,ind_maxautoval)));
1584 : }
1585 : } else {
1586 1606 : for(unsigned i=0; i<ncv; ++i) {
1587 970 : cutoff.push_back(std::sqrt(2.0*dp2cutoff)*hill.sigma[i]);
1588 : }
1589 : }
1590 :
1591 636 : if(doInt_) {
1592 2 : if(hill.center[0]+cutoff[0] > uppI_ || hill.center[0]-cutoff[0] < lowI_) {
1593 : // in this case, we updated the entire grid to avoid problems
1594 2 : return BiasGrid_->getNbin();
1595 : } else {
1596 0 : nneigh.push_back( static_cast<unsigned>(ceil(cutoff[0]/BiasGrid_->getDx()[0])) );
1597 : return nneigh;
1598 : }
1599 : } else {
1600 1602 : for(unsigned i=0; i<ncv; i++) {
1601 1936 : nneigh.push_back( static_cast<unsigned>(ceil(cutoff[i]/BiasGrid_->getDx()[i])) );
1602 : }
1603 : }
1604 :
1605 : return nneigh;
1606 : }
1607 :
1608 281 : double MetaD::getBias(const std::vector<double>& cv)
1609 : {
1610 281 : double bias=0.0;
1611 281 : if(grid_) bias = BiasGrid_->getValue(cv);
1612 : else {
1613 82 : unsigned nt=OpenMP::getNumThreads();
1614 82 : unsigned stride=comm.Get_size();
1615 82 : unsigned rank=comm.Get_rank();
1616 :
1617 82 : if(!nlist_) {
1618 82 : #pragma omp parallel num_threads(nt)
1619 : {
1620 : #pragma omp for reduction(+:bias) nowait
1621 : for(unsigned i=rank; i<hills_.size(); i+=stride) bias+=evaluateGaussian(cv,hills_[i]);
1622 : }
1623 : } else {
1624 0 : #pragma omp parallel num_threads(nt)
1625 : {
1626 : #pragma omp for reduction(+:bias) nowait
1627 : for(unsigned i=rank; i<nlist_hills_.size(); i+=stride) bias+=evaluateGaussian(cv,nlist_hills_[i]);
1628 : }
1629 : }
1630 82 : comm.Sum(bias);
1631 : }
1632 :
1633 281 : return bias;
1634 : }
1635 :
1636 8390 : double MetaD::getBiasAndDerivatives(const std::vector<double>& cv, std::vector<double>& der)
1637 : {
1638 8390 : unsigned ncv=getNumberOfArguments();
1639 8390 : double bias=0.0;
1640 8390 : if(grid_) {
1641 1501 : std::vector<double> vder(ncv);
1642 1501 : bias=BiasGrid_->getValueAndDerivatives(cv,vder);
1643 3483 : for(unsigned i=0; i<ncv; i++) der[i]=vder[i];
1644 : } else {
1645 6889 : unsigned nt=OpenMP::getNumThreads();
1646 6889 : unsigned stride=comm.Get_size();
1647 6889 : unsigned rank=comm.Get_rank();
1648 :
1649 6889 : if(!nlist_) {
1650 6884 : if(hills_.size()<2*nt*stride||nt==1) {
1651 : // for performance reasons and thread safety
1652 2587 : std::vector<double> dp(ncv);
1653 6704 : for(unsigned i=rank; i<hills_.size(); i+=stride) {
1654 4117 : bias+=evaluateGaussianAndDerivatives(cv,hills_[i],der,dp);
1655 : }
1656 : } else {
1657 4297 : #pragma omp parallel num_threads(nt)
1658 : {
1659 : std::vector<double> omp_deriv(ncv,0.);
1660 : // for performance reasons and thread safety
1661 : std::vector<double> dp(ncv);
1662 : #pragma omp for reduction(+:bias) nowait
1663 : for(unsigned i=rank; i<hills_.size(); i+=stride) {
1664 : bias+=evaluateGaussianAndDerivatives(cv,hills_[i],omp_deriv,dp);
1665 : }
1666 : #pragma omp critical
1667 : for(unsigned i=0; i<ncv; i++) der[i]+=omp_deriv[i];
1668 : }
1669 : }
1670 : } else {
1671 5 : if(hills_.size()<2*nt*stride||nt==1) {
1672 : // for performance reasons and thread safety
1673 0 : std::vector<double> dp(ncv);
1674 0 : for(unsigned i=rank; i<nlist_hills_.size(); i+=stride) {
1675 0 : bias+=evaluateGaussianAndDerivatives(cv,nlist_hills_[i],der,dp);
1676 : }
1677 : } else {
1678 5 : #pragma omp parallel num_threads(nt)
1679 : {
1680 : std::vector<double> omp_deriv(ncv,0.);
1681 : // for performance reasons and thread safety
1682 : std::vector<double> dp(ncv);
1683 : #pragma omp for reduction(+:bias) nowait
1684 : for(unsigned i=rank; i<nlist_hills_.size(); i+=stride) {
1685 : bias+=evaluateGaussianAndDerivatives(cv,nlist_hills_[i],omp_deriv,dp);
1686 : }
1687 : #pragma omp critical
1688 : for(unsigned i=0; i<ncv; i++) der[i]+=omp_deriv[i];
1689 : }
1690 : }
1691 : }
1692 6889 : comm.Sum(bias);
1693 6889 : comm.Sum(der);
1694 : }
1695 :
1696 8390 : return bias;
1697 : }
1698 :
1699 0 : double MetaD::getGaussianNormalization(const Gaussian& hill)
1700 : {
1701 : double norm=1;
1702 0 : unsigned ncv=hill.center.size();
1703 :
1704 0 : if(hill.multivariate) {
1705 : // recompose the full sigma from the upper diag cholesky
1706 : unsigned k=0;
1707 : Matrix<double> mymatrix(ncv,ncv);
1708 0 : for(unsigned i=0; i<ncv; i++) {
1709 0 : for(unsigned j=i; j<ncv; j++) {
1710 0 : mymatrix(i,j)=mymatrix(j,i)=hill.sigma[k]; // recompose the full inverse matrix
1711 0 : k++;
1712 : }
1713 0 : double ldet; logdet( mymatrix, ldet );
1714 0 : norm = std::exp( ldet ); // Not sure here if mymatrix is sigma or inverse
1715 : }
1716 : } else {
1717 0 : for(unsigned i=0; i<hill.sigma.size(); i++) norm*=hill.sigma[i];
1718 : }
1719 :
1720 0 : return norm*std::pow(2*pi,static_cast<double>(ncv)/2.0);
1721 : }
1722 :
1723 192 : double MetaD::evaluateGaussian(const std::vector<double>& cv, const Gaussian& hill)
1724 : {
1725 192 : unsigned ncv=cv.size();
1726 :
1727 : // I use a pointer here because cv is const (and should be const)
1728 : // but when using doInt it is easier to locally replace cv[0] with
1729 : // the upper/lower limit in case it is out of range
1730 : double tmpcv[1];
1731 : const double *pcv=NULL; // pointer to cv
1732 192 : if(ncv>0) pcv=&cv[0];
1733 192 : if(doInt_) {
1734 0 : plumed_assert(ncv==1);
1735 0 : tmpcv[0]=cv[0];
1736 0 : if(cv[0]<lowI_) tmpcv[0]=lowI_;
1737 0 : if(cv[0]>uppI_) tmpcv[0]=uppI_;
1738 : pcv=&(tmpcv[0]);
1739 : }
1740 :
1741 : double dp2=0.0;
1742 192 : if(hill.multivariate) {
1743 : unsigned k=0;
1744 : // recompose the full sigma from the upper diag cholesky
1745 : Matrix<double> mymatrix(ncv,ncv);
1746 0 : for(unsigned i=0; i<ncv; i++) {
1747 0 : for(unsigned j=i; j<ncv; j++) {
1748 0 : mymatrix(i,j)=mymatrix(j,i)=hill.sigma[k]; // recompose the full inverse matrix
1749 0 : k++;
1750 : }
1751 : }
1752 0 : for(unsigned i=0; i<ncv; i++) {
1753 0 : double dp_i=difference(i,hill.center[i],pcv[i]);
1754 0 : for(unsigned j=i; j<ncv; j++) {
1755 0 : if(i==j) {
1756 0 : dp2+=dp_i*dp_i*mymatrix(i,j)*0.5;
1757 : } else {
1758 0 : double dp_j=difference(j,hill.center[j],pcv[j]);
1759 0 : dp2+=dp_i*dp_j*mymatrix(i,j);
1760 : }
1761 : }
1762 : }
1763 : } else {
1764 576 : for(unsigned i=0; i<ncv; i++) {
1765 384 : double dp=difference(i,hill.center[i],pcv[i])*hill.invsigma[i];
1766 384 : dp2+=dp*dp;
1767 : }
1768 192 : dp2*=0.5;
1769 : }
1770 :
1771 : double bias=0.0;
1772 192 : if(dp2<dp2cutoff) bias=hill.height*(stretchA*std::exp(-dp2)+stretchB);
1773 :
1774 192 : return bias;
1775 : }
1776 :
1777 2408896 : double MetaD::evaluateGaussianAndDerivatives(const std::vector<double>& cv, const Gaussian& hill, std::vector<double>& der, std::vector<double>& dp_)
1778 : {
1779 2408896 : unsigned ncv=cv.size();
1780 :
1781 : // I use a pointer here because cv is const (and should be const)
1782 : // but when using doInt it is easier to locally replace cv[0] with
1783 : // the upper/lower limit in case it is out of range
1784 : const double *pcv=NULL; // pointer to cv
1785 : double tmpcv[1]; // tmp array with cv (to be used with doInt_)
1786 2408896 : if(ncv>0) pcv=&cv[0];
1787 2408896 : if(doInt_) {
1788 602 : plumed_assert(ncv==1);
1789 602 : tmpcv[0]=cv[0];
1790 602 : if(cv[0]<lowI_) tmpcv[0]=lowI_;
1791 602 : if(cv[0]>uppI_) tmpcv[0]=uppI_;
1792 : pcv=&(tmpcv[0]);
1793 : }
1794 :
1795 : bool int_der=false;
1796 2408896 : if(doInt_) {
1797 602 : if(cv[0]<lowI_ || cv[0]>uppI_) int_der=true;
1798 : }
1799 :
1800 : double dp2=0.0;
1801 : double bias=0.0;
1802 2408896 : if(hill.multivariate) {
1803 : unsigned k=0;
1804 : // recompose the full sigma from the upper diag cholesky
1805 : Matrix<double> mymatrix(ncv,ncv);
1806 161635 : for(unsigned i=0; i<ncv; i++) {
1807 162513 : for(unsigned j=i; j<ncv; j++) {
1808 81476 : mymatrix(i,j)=mymatrix(j,i)=hill.sigma[k]; // recompose the full inverse matrix
1809 81476 : k++;
1810 : }
1811 : }
1812 161635 : for(unsigned i=0; i<ncv; i++) {
1813 81037 : dp_[i]=difference(i,hill.center[i],pcv[i]);
1814 162513 : for(unsigned j=i; j<ncv; j++) {
1815 81476 : if(i==j) {
1816 81037 : dp2+=dp_[i]*dp_[i]*mymatrix(i,j)*0.5;
1817 : } else {
1818 439 : double dp_j=difference(j,hill.center[j],pcv[j]);
1819 439 : dp2+=dp_[i]*dp_j*mymatrix(i,j);
1820 : }
1821 : }
1822 : }
1823 80598 : if(dp2<dp2cutoff) {
1824 77683 : bias=hill.height*std::exp(-dp2);
1825 77683 : if(!int_der) {
1826 155673 : for(unsigned i=0; i<ncv; i++) {
1827 : double tmp=0.0;
1828 156594 : for(unsigned j=0; j<ncv; j++) tmp += dp_[j]*mymatrix(i,j)*bias;
1829 77990 : der[i]-=tmp*stretchA;
1830 : }
1831 : } else {
1832 0 : for(unsigned i=0; i<ncv; i++) der[i]=0.;
1833 : }
1834 77683 : bias=stretchA*bias+hill.height*stretchB;
1835 : }
1836 : } else {
1837 6975252 : for(unsigned i=0; i<ncv; i++) {
1838 4646954 : dp_[i]=difference(i,hill.center[i],pcv[i])*hill.invsigma[i];
1839 4646954 : dp2+=dp_[i]*dp_[i];
1840 : }
1841 2328298 : dp2*=0.5;
1842 2328298 : if(dp2<dp2cutoff) {
1843 1356532 : bias=hill.height*std::exp(-dp2);
1844 1356532 : if(!int_der) {
1845 4060817 : for(unsigned i=0; i<ncv; i++) der[i]-=bias*dp_[i]*hill.invsigma[i]*stretchA;
1846 : } else {
1847 478 : for(unsigned i=0; i<ncv; i++) der[i]=0.;
1848 : }
1849 1356532 : bias=stretchA*bias+hill.height*stretchB;
1850 : }
1851 : }
1852 :
1853 2408896 : return bias;
1854 : }
1855 :
1856 2732 : double MetaD::getHeight(const std::vector<double>& cv)
1857 : {
1858 2732 : double height=height0_;
1859 2732 : if(welltemp_) {
1860 271 : double vbias = getBias(cv);
1861 271 : if(biasf_>1.0) {
1862 255 : height = height0_*std::exp(-vbias/(kbt_*(biasf_-1.0)));
1863 : } else {
1864 : // notice that if gamma=1 we store directly -F
1865 16 : height = height0_*std::exp(-vbias/kbt_);
1866 : }
1867 : }
1868 2732 : if(dampfactor_>0.0) {
1869 18 : plumed_assert(BiasGrid_);
1870 18 : double m=BiasGrid_->getMaxValue();
1871 18 : height*=std::exp(-m/(kbt_*(dampfactor_)));
1872 : }
1873 2732 : if (tt_specs_.is_active) {
1874 60 : double vbarrier = transition_bias_;
1875 60 : temperHeight(height, tt_specs_, vbarrier);
1876 : }
1877 2732 : if(TargetGrid_) {
1878 18 : double f=TargetGrid_->getValue(cv)-TargetGrid_->getMaxValue();
1879 18 : height*=std::exp(f/kbt_);
1880 : }
1881 2732 : return height;
1882 : }
1883 :
1884 60 : void MetaD::temperHeight(double& height, const TemperingSpecs& t_specs, const double tempering_bias)
1885 : {
1886 60 : if (t_specs.alpha == 1.0) {
1887 80 : height *= std::exp(-std::max(0.0, tempering_bias - t_specs.threshold) / (kbt_ * (t_specs.biasf - 1.0)));
1888 : } else {
1889 40 : height *= std::pow(1 + (1 - t_specs.alpha) / t_specs.alpha * std::max(0.0, tempering_bias - t_specs.threshold) / (kbt_ * (t_specs.biasf - 1.0)), - t_specs.alpha / (1 - t_specs.alpha));
1890 : }
1891 60 : }
1892 :
1893 8430 : void MetaD::calculate()
1894 : {
1895 : // this is because presently there is no way to properly pass information
1896 : // on adaptive hills (diff) after exchanges:
1897 8430 : if(adaptive_==FlexibleBin::diffusion && getExchangeStep()) error("ADAPTIVE=DIFF is not compatible with replica exchange");
1898 :
1899 8430 : const unsigned ncv=getNumberOfArguments();
1900 8430 : std::vector<double> cv(ncv);
1901 21067 : for(unsigned i=0; i<ncv; ++i) cv[i]=getArgument(i);
1902 :
1903 8430 : if(nlist_) {
1904 5 : nlist_steps_++;
1905 5 : if(getExchangeStep()) nlist_update_=true;
1906 : else {
1907 11 : for(unsigned i=0; i<ncv; ++i) {
1908 8 : double d = difference(i, cv[i], nlist_center_[i]);
1909 8 : double nk_dist2 = d*d/nlist_dev2_[i];
1910 8 : if(nk_dist2>nlist_param_[1]) {nlist_update_=true; break;}
1911 : }
1912 : }
1913 5 : if(nlist_update_) updateNlist();
1914 : }
1915 :
1916 : double ene = 0.;
1917 8430 : std::vector<double> der(ncv,0.);
1918 8430 : if(biasf_!=1.0) ene = getBiasAndDerivatives(cv,der);
1919 : setBias(ene);
1920 21067 : for(unsigned i=0; i<ncv; i++) setOutputForce(i,-der[i]);
1921 :
1922 8435 : if(calc_work_) getPntrToComponent("work")->set(work_);
1923 8540 : if(calc_rct_) getPntrToComponent("rbias")->set(ene - reweight_factor_);
1924 : // calculate the acceleration factor
1925 8430 : if(acceleration_&&!isFirstStep_) {
1926 329 : acc_ += static_cast<double>(getStride()) * std::exp(ene/(kbt_));
1927 329 : const double mean_acc = acc_/((double) getStep());
1928 329 : getPntrToComponent("acc")->set(mean_acc);
1929 8430 : } else if (acceleration_ && isFirstStep_ && acc_restart_mean_ > 0.0) {
1930 2 : acc_ = acc_restart_mean_ * static_cast<double>(getStep());
1931 2 : if(freq_adaptive_) {
1932 : // has to be done here if restarting, as the acc is not defined before
1933 1 : updateFrequencyAdaptiveStride();
1934 : }
1935 : }
1936 8430 : }
1937 :
1938 6234 : void MetaD::update()
1939 : {
1940 : // adding hills criteria (could be more complex though)
1941 : bool nowAddAHill;
1942 6234 : if(getStep()%current_stride_==0 && !isFirstStep_) nowAddAHill=true;
1943 : else {
1944 : nowAddAHill=false;
1945 3502 : isFirstStep_=false;
1946 : }
1947 :
1948 6234 : unsigned ncv=getNumberOfArguments();
1949 6234 : std::vector<double> cv(ncv);
1950 16675 : for(unsigned i=0; i<ncv; ++i) cv[i] = getArgument(i);
1951 :
1952 : double vbias=0.;
1953 6234 : if(calc_work_) vbias=getBias(cv);
1954 :
1955 : // if you use adaptive, call the FlexibleBin
1956 : bool multivariate=false;
1957 6234 : if(adaptive_!=FlexibleBin::none) {
1958 778 : flexbin_->update(nowAddAHill);
1959 : multivariate=true;
1960 : }
1961 :
1962 : std::vector<double> thissigma;
1963 6234 : if(nowAddAHill) {
1964 : // add a Gaussian
1965 2732 : double height=getHeight(cv);
1966 : // returns upper diagonal inverse
1967 3106 : if(adaptive_!=FlexibleBin::none) thissigma=flexbin_->getInverseMatrix();
1968 : // returns normal sigma
1969 2358 : else thissigma=sigma0_;
1970 :
1971 : // In case we use walkers_mpi, it is now necessary to communicate with other replicas.
1972 2732 : if(walkers_mpi_) {
1973 : // Allocate arrays to store all walkers hills
1974 174 : std::vector<double> all_cv(mpi_nw_*ncv,0.0);
1975 174 : std::vector<double> all_sigma(mpi_nw_*thissigma.size(),0.0);
1976 174 : std::vector<double> all_height(mpi_nw_,0.0);
1977 174 : std::vector<int> all_multivariate(mpi_nw_,0);
1978 174 : if(comm.Get_rank()==0) {
1979 : // Communicate (only root)
1980 99 : multi_sim_comm.Allgather(cv,all_cv);
1981 99 : multi_sim_comm.Allgather(thissigma,all_sigma);
1982 : // notice that if gamma=1 we store directly -F so this scaling is not necessary:
1983 99 : multi_sim_comm.Allgather(height*(biasf_>1.0?biasf_/(biasf_-1.0):1.0),all_height);
1984 99 : multi_sim_comm.Allgather(int(multivariate),all_multivariate);
1985 : }
1986 : // Share info with group members
1987 174 : comm.Bcast(all_cv,0);
1988 174 : comm.Bcast(all_sigma,0);
1989 174 : comm.Bcast(all_height,0);
1990 174 : comm.Bcast(all_multivariate,0);
1991 :
1992 : // Flying Gaussian
1993 174 : if (flying_) {
1994 54 : hills_.clear();
1995 54 : comm.Barrier();
1996 : }
1997 :
1998 696 : for(unsigned i=0; i<mpi_nw_; i++) {
1999 : // actually add hills one by one
2000 522 : std::vector<double> cv_now(ncv);
2001 522 : std::vector<double> sigma_now(thissigma.size());
2002 1566 : for(unsigned j=0; j<ncv; j++) cv_now[j]=all_cv[i*ncv+j];
2003 1674 : for(unsigned j=0; j<thissigma.size(); j++) sigma_now[j]=all_sigma[i*thissigma.size()+j];
2004 : // notice that if gamma=1 we store directly -F so this scaling is not necessary:
2005 522 : double fact=(biasf_>1.0?(biasf_-1.0)/biasf_:1.0);
2006 522 : Gaussian newhill=Gaussian(all_multivariate[i],all_height[i]*fact,cv_now,sigma_now);
2007 522 : addGaussian(newhill);
2008 522 : if(!flying_) writeGaussian(newhill,hillsOfile_);
2009 522 : }
2010 : } else {
2011 2558 : Gaussian newhill=Gaussian(multivariate,height,cv,thissigma);
2012 2558 : addGaussian(newhill);
2013 2558 : writeGaussian(newhill,hillsOfile_);
2014 2558 : }
2015 :
2016 : // this is to update the hills neighbor list
2017 2732 : if(nlist_) nlist_update_=true;
2018 : }
2019 :
2020 : // this should be outside of the if block in case
2021 : // mw_rstride_ is not a multiple of stride_
2022 6234 : if(mw_n_>1 && getStep()%mw_rstride_==0) hillsOfile_.flush();
2023 :
2024 6234 : if(calc_work_) {
2025 5 : if(nlist_) updateNlist();
2026 5 : double vbias1=getBias(cv);
2027 5 : work_+=vbias1-vbias;
2028 : }
2029 :
2030 : // dump grid on file
2031 6234 : if(wgridstride_>0&&(getStep()%wgridstride_==0||getCPT())) {
2032 : // in case old grids are stored, a sequence of grids should appear
2033 : // this call results in a repetition of the header:
2034 90 : if(storeOldGrids_) gridfile_.clearFields();
2035 : // in case only latest grid is stored, file should be rewound
2036 : // this will overwrite previously written grids
2037 : else {
2038 50 : int r = 0;
2039 50 : if(walkers_mpi_) {
2040 0 : if(comm.Get_rank()==0) r=multi_sim_comm.Get_rank();
2041 0 : comm.Bcast(r,0);
2042 : }
2043 50 : if(r==0) gridfile_.rewind();
2044 : }
2045 90 : BiasGrid_->writeToFile(gridfile_);
2046 : // if a single grid is stored, it is necessary to flush it, otherwise
2047 : // the file might stay empty forever (when a single grid is not large enough to
2048 : // trigger flushing from the operating system).
2049 : // on the other hand, if grids are stored one after the other this is
2050 : // no necessary, and we leave the flushing control to the user as usual
2051 : // (with FLUSH keyword)
2052 90 : if(!storeOldGrids_) gridfile_.flush();
2053 : }
2054 :
2055 : // if multiple walkers and time to read Gaussians
2056 6234 : if(mw_n_>1 && getStep()%mw_rstride_==0) {
2057 12048 : for(int i=0; i<mw_n_; ++i) {
2058 : // don't read your own Gaussians
2059 9036 : if(i==mw_id_) continue;
2060 : // if the file is not open yet
2061 6024 : if(!(ifiles_[i]->isOpen())) {
2062 : // check if it exists now and open it!
2063 7 : if(ifiles_[i]->FileExist(ifilesnames_[i])) {
2064 7 : ifiles_[i]->open(ifilesnames_[i]);
2065 7 : ifiles_[i]->reset(false);
2066 : }
2067 : // otherwise read the new Gaussians
2068 : } else {
2069 6017 : log.printf(" Reading hills from %s:",ifilesnames_[i].c_str());
2070 6017 : readGaussians(ifiles_[i].get());
2071 6017 : ifiles_[i]->reset(false);
2072 : }
2073 : }
2074 : // this is to update the hills neighbor list
2075 3012 : if(nlist_) nlist_update_=true;
2076 : }
2077 :
2078 : // Recalculate special bias quantities whenever the bias has been changed by the update.
2079 6234 : bool bias_has_changed = (nowAddAHill || (mw_n_ > 1 && getStep() % mw_rstride_ == 0));
2080 6234 : if (calc_rct_ && bias_has_changed && getStep()%(stride_*rct_ustride_)==0) computeReweightingFactor();
2081 6234 : if (calc_max_bias_ && bias_has_changed) {
2082 0 : max_bias_ = BiasGrid_->getMaxValue();
2083 0 : getPntrToComponent("maxbias")->set(max_bias_);
2084 : }
2085 6234 : if (calc_transition_bias_ && bias_has_changed) {
2086 260 : transition_bias_ = getTransitionBarrierBias();
2087 520 : getPntrToComponent("transbias")->set(transition_bias_);
2088 : }
2089 :
2090 : // Frequency adaptive metadynamics - update hill addition frequency
2091 6234 : if(freq_adaptive_ && getStep()%fa_update_frequency_==0) {
2092 151 : updateFrequencyAdaptiveStride();
2093 : }
2094 6234 : }
2095 :
2096 : /// takes a pointer to the file and a template std::string with values v and gives back the next center, sigma and height
2097 8156 : bool MetaD::scanOneHill(IFile* ifile, std::vector<Value>& tmpvalues, std::vector<double>& center, std::vector<double>& sigma, double& height, bool& multivariate)
2098 : {
2099 : double dummy;
2100 8156 : multivariate=false;
2101 16312 : if(ifile->scanField("time",dummy)) {
2102 2120 : unsigned ncv=tmpvalues.size();
2103 6314 : for(unsigned i=0; i<ncv; ++i) {
2104 4194 : ifile->scanField( &tmpvalues[i] );
2105 4194 : if( tmpvalues[i].isPeriodic() && ! getPntrToArgument(i)->isPeriodic() ) {
2106 0 : error("in hills file periodicity for variable " + tmpvalues[i].getName() + " does not match periodicity in input");
2107 4194 : } else if( tmpvalues[i].isPeriodic() ) {
2108 0 : std::string imin, imax; tmpvalues[i].getDomain( imin, imax );
2109 0 : std::string rmin, rmax; getPntrToArgument(i)->getDomain( rmin, rmax );
2110 0 : if( imin!=rmin || imax!=rmax ) {
2111 0 : error("in hills file periodicity for variable " + tmpvalues[i].getName() + " does not match periodicity in input");
2112 : }
2113 : }
2114 4194 : center[i]=tmpvalues[i].get();
2115 : }
2116 : // scan for kerneltype
2117 2120 : std::string ktype="stretched-gaussian";
2118 6351 : if( ifile->FieldExist("kerneltype") ) ifile->scanField("kerneltype",ktype);
2119 2120 : if( ktype=="gaussian" ) {
2120 12 : noStretchWarning();
2121 2108 : } else if( ktype!="stretched-gaussian") {
2122 0 : error("non Gaussian kernels are not supported in MetaD");
2123 : }
2124 : // scan for multivariate label: record the actual file position so to eventually rewind
2125 : std::string sss;
2126 4240 : ifile->scanField("multivariate",sss);
2127 2120 : if(sss=="true") multivariate=true;
2128 2120 : else if(sss=="false") multivariate=false;
2129 0 : else plumed_merror("cannot parse multivariate = "+ sss);
2130 2120 : if(multivariate) {
2131 0 : sigma.resize(ncv*(ncv+1)/2);
2132 : Matrix<double> upper(ncv,ncv);
2133 : Matrix<double> lower(ncv,ncv);
2134 0 : for(unsigned i=0; i<ncv; i++) {
2135 0 : for(unsigned j=0; j<ncv-i; j++) {
2136 0 : ifile->scanField("sigma_"+getPntrToArgument(j+i)->getName()+"_"+getPntrToArgument(j)->getName(),lower(j+i,j));
2137 0 : upper(j,j+i)=lower(j+i,j);
2138 : }
2139 : }
2140 : Matrix<double> mymult(ncv,ncv);
2141 : Matrix<double> invmatrix(ncv,ncv);
2142 0 : mult(lower,upper,mymult);
2143 : // now invert and get the sigmas
2144 0 : Invert(mymult,invmatrix);
2145 : // put the sigmas in the usual order: upper diagonal (this time in normal form and not in band form)
2146 : unsigned k=0;
2147 0 : for(unsigned i=0; i<ncv; i++) {
2148 0 : for(unsigned j=i; j<ncv; j++) {
2149 0 : sigma[k]=invmatrix(i,j);
2150 0 : k++;
2151 : }
2152 : }
2153 : } else {
2154 6314 : for(unsigned i=0; i<ncv; ++i) {
2155 8388 : ifile->scanField("sigma_"+getPntrToArgument(i)->getName(),sigma[i]);
2156 : }
2157 : }
2158 :
2159 2120 : ifile->scanField("height",height);
2160 2120 : ifile->scanField("biasf",dummy);
2161 6177 : if(ifile->FieldExist("clock")) ifile->scanField("clock",dummy);
2162 4240 : if(ifile->FieldExist("lower_int")) ifile->scanField("lower_int",dummy);
2163 4240 : if(ifile->FieldExist("upper_int")) ifile->scanField("upper_int",dummy);
2164 2120 : ifile->scanField();
2165 : return true;
2166 : } else {
2167 : return false;
2168 : }
2169 : }
2170 :
2171 102 : void MetaD::computeReweightingFactor()
2172 : {
2173 102 : if(biasf_==1.0) { // in this case we have no bias, so reweight factor is 0.0
2174 0 : getPntrToComponent("rct")->set(0.0);
2175 0 : return;
2176 : }
2177 :
2178 102 : double Z_0=0; //proportional to the integral of exp(-beta*F)
2179 102 : double Z_V=0; //proportional to the integral of exp(-beta*(F+V))
2180 102 : double minusBetaF=biasf_/(biasf_-1.)/kbt_;
2181 102 : double minusBetaFplusV=1./(biasf_-1.)/kbt_;
2182 102 : if (biasf_==-1.0) { //non well-tempered case
2183 0 : minusBetaF=1./kbt_;
2184 : minusBetaFplusV=0;
2185 : }
2186 102 : max_bias_=BiasGrid_->getMaxValue(); //to avoid exp overflow
2187 :
2188 102 : const unsigned rank=comm.Get_rank();
2189 102 : const unsigned stride=comm.Get_size();
2190 920504 : for (Grid::index_t t=rank; t<BiasGrid_->getSize(); t+=stride) {
2191 920402 : const double val=BiasGrid_->getValue(t);
2192 920402 : Z_0+=std::exp(minusBetaF*(val-max_bias_));
2193 920402 : Z_V+=std::exp(minusBetaFplusV*(val-max_bias_));
2194 : }
2195 102 : comm.Sum(Z_0);
2196 102 : comm.Sum(Z_V);
2197 :
2198 102 : reweight_factor_=kbt_*std::log(Z_0/Z_V)+max_bias_;
2199 204 : getPntrToComponent("rct")->set(reweight_factor_);
2200 : }
2201 :
2202 273 : double MetaD::getTransitionBarrierBias()
2203 : {
2204 : // If there is only one well of interest, return the bias at that well point.
2205 273 : if (transitionwells_.size() == 1) {
2206 0 : double tb_bias = getBias(transitionwells_[0]);
2207 0 : return tb_bias;
2208 :
2209 : // Otherwise, check for the least barrier bias between all pairs of wells.
2210 : // Note that because the paths can be considered edges between the wells' nodes
2211 : // to make a graph and the path barriers satisfy certain cycle inequalities, it
2212 : // is sufficient to look at paths corresponding to a minimal spanning tree of the
2213 : // overall graph rather than examining every edge in the graph.
2214 : // For simplicity, I chose the star graph with center well 0 as the spanning tree.
2215 : // It is most efficient to start the path searches from the wells that are
2216 : // expected to be sampled last, so transitionwell_[0] should correspond to the
2217 : // starting well. With this choice the searches will terminate in one step until
2218 : // transitionwell_[1] is sampled.
2219 : } else {
2220 : double least_transition_bias;
2221 273 : std::vector<double> sink = transitionwells_[0];
2222 273 : std::vector<double> source = transitionwells_[1];
2223 273 : least_transition_bias = BiasGrid_->findMaximalPathMinimum(source, sink);
2224 273 : for (unsigned i = 2; i < transitionwells_.size(); i++) {
2225 0 : if (least_transition_bias == 0.0) {
2226 : break;
2227 : }
2228 0 : source = transitionwells_[i];
2229 0 : double curr_transition_bias = BiasGrid_->findMaximalPathMinimum(source, sink);
2230 0 : least_transition_bias = fmin(curr_transition_bias, least_transition_bias);
2231 : }
2232 : return least_transition_bias;
2233 : }
2234 : }
2235 :
2236 154 : void MetaD::updateFrequencyAdaptiveStride()
2237 : {
2238 154 : plumed_massert(freq_adaptive_,"should only be used if frequency adaptive metadynamics is enabled");
2239 154 : plumed_massert(acceleration_,"frequency adaptive metadynamics can only be used if the acceleration factor is calculated");
2240 154 : const double mean_acc = acc_/((double) getStep());
2241 154 : int tmp_stride= stride_*floor((mean_acc/fa_min_acceleration_)+0.5);
2242 154 : if(mean_acc >= fa_min_acceleration_) {
2243 129 : if(tmp_stride > current_stride_) {current_stride_ = tmp_stride;}
2244 : }
2245 154 : if(fa_max_stride_!=0 && current_stride_>fa_max_stride_) {
2246 0 : current_stride_=fa_max_stride_;
2247 : }
2248 154 : getPntrToComponent("pace")->set(current_stride_);
2249 154 : }
2250 :
2251 8430 : bool MetaD::checkNeedsGradients()const
2252 : {
2253 8430 : if(adaptive_==FlexibleBin::geometry) {
2254 192 : if(getStep()%stride_==0 && !isFirstStep_) return true;
2255 109 : else return false;
2256 : } else return false;
2257 : }
2258 :
2259 4 : void MetaD::updateNlist()
2260 : {
2261 : // no need to check for neighbors
2262 4 : if(hills_.size()==0) return;
2263 :
2264 : // here we generate the neighbor list
2265 4 : nlist_hills_.clear();
2266 : std::vector<Gaussian> local_flat_nl;
2267 4 : unsigned nt=OpenMP::getNumThreads();
2268 4 : if(hills_.size()<2*nt) nt=1;
2269 4 : #pragma omp parallel num_threads(nt)
2270 : {
2271 : std::vector<Gaussian> private_flat_nl;
2272 : #pragma omp for nowait
2273 : for(unsigned k=0; k<hills_.size(); k++)
2274 : {
2275 : double dist2=0;
2276 : for(unsigned i=0; i<getNumberOfArguments(); i++)
2277 : {
2278 : const double d=difference(i,getArgument(i),hills_[k].center[i])/hills_[k].sigma[i];
2279 : dist2+=d*d;
2280 : }
2281 : if(dist2<=nlist_param_[0]*dp2cutoff) private_flat_nl.push_back(hills_[k]);
2282 : }
2283 : #pragma omp critical
2284 : local_flat_nl.insert(local_flat_nl.end(), private_flat_nl.begin(), private_flat_nl.end());
2285 : }
2286 4 : nlist_hills_ = local_flat_nl;
2287 :
2288 : // here we set some properties that are used to decide when to update it again
2289 12 : for(unsigned i=0; i<getNumberOfArguments(); i++) nlist_center_[i]=getArgument(i);
2290 : std::vector<double> dev2;
2291 4 : dev2.resize(getNumberOfArguments(),0);
2292 46 : for(unsigned k=0; k<nlist_hills_.size(); k++)
2293 : {
2294 126 : for(unsigned i=0; i<getNumberOfArguments(); i++)
2295 : {
2296 84 : const double d=difference(i,getArgument(i),nlist_hills_[k].center[i]);
2297 84 : dev2[i]+=d*d;
2298 : }
2299 : }
2300 12 : for(unsigned i=0; i<getNumberOfArguments(); i++) {
2301 8 : if(dev2[i]>0.) nlist_dev2_[i]=dev2[i]/static_cast<double>(nlist_hills_.size());
2302 0 : else nlist_dev2_[i]=hills_.back().sigma[i]*hills_.back().sigma[i];
2303 : }
2304 :
2305 : // we are done
2306 4 : getPntrToComponent("nlker")->set(nlist_hills_.size());
2307 4 : getPntrToComponent("nlsteps")->set(nlist_steps_);
2308 4 : nlist_steps_=0;
2309 4 : nlist_update_=false;
2310 4 : }
2311 :
2312 : }
2313 : }
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