Update Colvars to version 2022-05-09
This update includes one new feature (neural-network based collective variables), several small enhancements (including an automatic definition of grid boundaries for angle-based CVs, and a normalization option for eigenvector-based CVs), bugfixes and documentation improvements. Usage information for specific features included in the Colvars library (i.e. not just the library as a whole) is now also reported to the screen or LAMMPS logfile (as is done already in other LAMMPS classes). Notable to LAMMPS code development are the removals of duplicated code and of ambiguously-named preprocessor defines in the Colvars headers. Since the last PR, the existing regression tests have also been running automatically via GitHub Actions. The following pull requests in the Colvars repository are relevant to LAMMPS: - 475 Remove fatal error condition https://github.com/Colvars/colvars/pull/475 (@jhenin, @giacomofiorin) - 474 Allow normalizing eigenvector vector components to deal with unit change https://github.com/Colvars/colvars/pull/474 (@giacomofiorin, @jhenin) - 470 Better error handling in the initialization of NeuralNetwork CV https://github.com/Colvars/colvars/pull/470 (@HanatoK) - 468 Add examples of histogram configuration, with and without explicit grid parameters https://github.com/Colvars/colvars/pull/468 (@giacomofiorin) - 464 Fix #463 using more fine-grained features https://github.com/Colvars/colvars/pull/464 (@jhenin, @giacomofiorin) - 447 [RFC] New option "scaledBiasingForce" for colvarbias https://github.com/Colvars/colvars/pull/447 (@HanatoK, @jhenin) - 444 [RFC] Implementation of dense neural network as CV https://github.com/Colvars/colvars/pull/444 (@HanatoK, @giacomofiorin, @jhenin) - 443 Fix explicit gradient dependency of sub-CVs https://github.com/Colvars/colvars/pull/443 (@HanatoK, @jhenin) - 442 Persistent bias count https://github.com/Colvars/colvars/pull/442 (@jhenin, @giacomofiorin) - 437 Return type of bias from scripting interface https://github.com/Colvars/colvars/pull/437 (@giacomofiorin) - 434 More flexible use of boundaries from colvars by grids https://github.com/Colvars/colvars/pull/434 (@jhenin) - 433 Prevent double-free in linearCombination https://github.com/Colvars/colvars/pull/433 (@HanatoK) - 428 More complete documentation for index file format (NDX) https://github.com/Colvars/colvars/pull/428 (@giacomofiorin) - 426 Integrate functional version of backup_file() into base proxy class https://github.com/Colvars/colvars/pull/426 (@giacomofiorin) - 424 Track CVC inheritance when documenting feature usage https://github.com/Colvars/colvars/pull/424 (@giacomofiorin) - 419 Generate citation report while running computations https://github.com/Colvars/colvars/pull/419 (@giacomofiorin, @jhenin) - 415 Rebin metadynamics bias from explicit hills when available https://github.com/Colvars/colvars/pull/415 (@giacomofiorin) - 312 Ignore a keyword if it has content to the left of it (regardless of braces) https://github.com/Colvars/colvars/pull/312 (@giacomofiorin) Authors: @giacomofiorin, @HanatoK, @jhenin
This commit is contained in:
Giacomo Fiorin
@ -39,6 +39,7 @@ colvarbias_abf::colvarbias_abf(char const *key)
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int colvarbias_abf::init(std::string const &conf)
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{
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colvarbias::init(conf);
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cvm::main()->cite_feature("ABF colvar bias implementation");
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colvarproxy *proxy = cvm::main()->proxy;
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@ -81,11 +82,11 @@ int colvarbias_abf::init(std::string const &conf)
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if (history_freq != 0) {
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if (output_freq == 0) {
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cvm::error("Error: historyFreq must be a multiple of outputFreq.\n",
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INPUT_ERROR);
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COLVARS_INPUT_ERROR);
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} else {
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if ((history_freq % output_freq) != 0) {
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cvm::error("Error: historyFreq must be a multiple of outputFreq.\n",
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INPUT_ERROR);
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COLVARS_INPUT_ERROR);
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}
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}
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}
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@ -94,10 +95,11 @@ int colvarbias_abf::init(std::string const &conf)
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// shared ABF
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get_keyval(conf, "shared", shared_on, false);
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if (shared_on) {
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cvm::main()->cite_feature("Multiple-walker ABF implementation");
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if ((proxy->replica_enabled() != COLVARS_OK) ||
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(proxy->num_replicas() <= 1)) {
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return cvm::error("Error: shared ABF requires more than one replica.",
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INPUT_ERROR);
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COLVARS_INPUT_ERROR);
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}
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cvm::log("shared ABF will be applied among "+
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cvm::to_str(proxy->num_replicas()) + " replicas.\n");
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@ -156,6 +158,12 @@ int colvarbias_abf::init(std::string const &conf)
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// and make it just a warning if some parameter is set?
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}
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if (b_extended) {
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cvm::main()->cite_feature("eABF implementation");
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} else {
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cvm::main()->cite_feature("Internal-forces free energy estimator");
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}
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if (get_keyval(conf, "maxForce", max_force)) {
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if (max_force.size() != num_variables()) {
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cvm::error("Error: Number of parameters to maxForce does not match number of colvars.");
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@ -188,6 +196,9 @@ int colvarbias_abf::init(std::string const &conf)
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// Data for eAB F z-based estimator
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if ( b_extended ) {
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get_keyval(conf, "CZARestimator", b_CZAR_estimator, true);
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if ( b_CZAR_estimator ) {
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cvm::main()->cite_feature("CZAR eABF estimator");
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}
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// CZAR output files for stratified eABF
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get_keyval(conf, "writeCZARwindowFile", b_czar_window_file, false,
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colvarparse::parse_silent);
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@ -214,7 +225,7 @@ int colvarbias_abf::init(std::string const &conf)
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czar_pmf = new integrate_potential(colvars, czar_gradients);
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}
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// Parameters for integrating initial (and final) gradient data
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get_keyval(conf, "integrateMaxIterations", integrate_iterations, 1e4, colvarparse::parse_silent);
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get_keyval(conf, "integrateMaxIterations", integrate_iterations, 10000, colvarparse::parse_silent);
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get_keyval(conf, "integrateTol", integrate_tol, 1e-6, colvarparse::parse_silent);
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// Projected ABF, updating the integrated PMF on the fly
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get_keyval(conf, "pABFintegrateFreq", pabf_freq, 0, colvarparse::parse_silent);
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@ -243,20 +254,21 @@ int colvarbias_abf::init(std::string const &conf)
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get_keyval(conf, "UIestimator", b_UI_estimator, false);
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if (b_UI_estimator) {
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std::vector<double> UI_lowerboundary;
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std::vector<double> UI_upperboundary;
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std::vector<double> UI_width;
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std::vector<double> UI_krestr;
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bool UI_restart = (input_prefix.size() > 0);
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cvm::main()->cite_feature("Umbrella-integration eABF estimator");
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std::vector<double> UI_lowerboundary;
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std::vector<double> UI_upperboundary;
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std::vector<double> UI_width;
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std::vector<double> UI_krestr;
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for (i = 0; i < num_variables(); i++)
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{
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UI_lowerboundary.push_back(colvars[i]->lower_boundary);
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UI_upperboundary.push_back(colvars[i]->upper_boundary);
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UI_width.push_back(colvars[i]->width);
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UI_krestr.push_back(colvars[i]->force_constant());
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}
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bool UI_restart = (input_prefix.size() > 0);
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for (i = 0; i < num_variables(); i++) {
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UI_lowerboundary.push_back(colvars[i]->lower_boundary);
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UI_upperboundary.push_back(colvars[i]->upper_boundary);
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UI_width.push_back(colvars[i]->width);
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UI_krestr.push_back(colvars[i]->force_constant());
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}
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eabf_UI = UIestimator::UIestimator(UI_lowerboundary,
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UI_upperboundary,
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UI_width,
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@ -412,7 +424,7 @@ int colvarbias_abf::update()
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// Compute and apply the new bias, if applicable
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if (is_enabled(f_cvb_apply_force) && samples->index_ok(bin)) {
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cvm::real count = samples->value(bin);
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cvm::real count = cvm::real(samples->value(bin));
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cvm::real fact = 1.0;
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// Factor that ensures smooth introduction of the force
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@ -442,13 +454,13 @@ int colvarbias_abf::update()
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// This is enforced naturally if using integrated PMF
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colvar_forces[0].real_value = fact * (grad[0] - gradients->average ());
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} else {
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for (size_t i = 0; i < num_variables(); i++) {
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for (i = 0; i < num_variables(); i++) {
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// subtracting the mean force (opposite of the FE gradient) means adding the gradient
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colvar_forces[i].real_value = fact * grad[i];
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}
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}
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if (cap_force) {
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for (size_t i = 0; i < num_variables(); i++) {
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for (i = 0; i < num_variables(); i++) {
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if ( colvar_forces[i].real_value * colvar_forces[i].real_value > max_force[i] * max_force[i] ) {
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colvar_forces[i].real_value = (colvar_forces[i].real_value > 0 ? max_force[i] : -1.0 * max_force[i]);
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}
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@ -484,7 +496,7 @@ int colvarbias_abf::update()
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{
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std::vector<double> x(num_variables(),0);
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std::vector<double> y(num_variables(),0);
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for (size_t i = 0; i < num_variables(); i++)
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for (i = 0; i < num_variables(); i++)
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{
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x[i] = colvars[i]->actual_value();
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y[i] = colvars[i]->value();
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@ -589,7 +601,7 @@ template <class T> int colvarbias_abf::write_grid_to_file(T const *grid,
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bool close) {
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std::ostream *os = cvm::proxy->output_stream(filename);
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if (!os) {
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return cvm::error("Error opening file " + filename + " for writing.\n", COLVARS_ERROR | FILE_ERROR);
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return cvm::error("Error opening file " + filename + " for writing.\n", COLVARS_ERROR | COLVARS_FILE_ERROR);
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}
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grid->write_multicol(*os);
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if (close) {
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@ -607,7 +619,7 @@ template <class T> int colvarbias_abf::write_grid_to_file(T const *grid,
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std::string dx = filename + ".dx";
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std::ostream *dx_os = cvm::proxy->output_stream(dx);
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if (!dx_os) {
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return cvm::error("Error opening file " + dx + " for writing.\n", COLVARS_ERROR | FILE_ERROR);
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return cvm::error("Error opening file " + dx + " for writing.\n", COLVARS_ERROR | COLVARS_FILE_ERROR);
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}
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grid->write_opendx(*dx_os);
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// if (close) {
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@ -709,7 +721,7 @@ void colvarbias_abf::read_gradients_samples()
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is.open(gradients_in_name.c_str());
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if (!is.is_open()) {
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cvm::error("Error opening ABF gradient file " +
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gradients_in_name + " for reading", INPUT_ERROR);
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gradients_in_name + " for reading", COLVARS_INPUT_ERROR);
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} else {
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gradients->read_multicol(is, true);
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is.close();
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@ -765,7 +777,7 @@ std::ostream & colvarbias_abf::write_state_data(std::ostream& os)
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std::istream & colvarbias_abf::read_state_data(std::istream& is)
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{
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if ( input_prefix.size() > 0 ) {
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cvm::error("ERROR: cannot provide both inputPrefix and a colvars state file.\n", INPUT_ERROR);
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cvm::error("ERROR: cannot provide both inputPrefix and a colvars state file.\n", COLVARS_INPUT_ERROR);
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}
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if (! read_state_data_key(is, "samples")) {
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