Mode.cpp

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// n2p2 - A neural network potential package
// Copyright (C) 2018 Andreas Singraber (University of Vienna)
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
#include "Mode.h"
#include "NeuralNetwork.h"
#include "utility.h"
#include "version.h"
#include <cmath>
#ifdef _OPENMP
#include <omp.h>
#endif
#include <Eigen/QR>
#include <algorithm> // std::min, std::max, std::remove_if
#include <cstdlib>   // atoi, atof
#include <fstream>   // std::ifstream
#ifndef N2P2_NO_SF_CACHE
#include <map>       // std::multimap
#endif
#include <limits>    // std::numeric_limits
#include <stdexcept> // std::runtime_error
#include <utility>   // std::piecewise_construct, std::forward_as_tuple
 
using namespace std;
using namespace nnp;
using namespace Eigen;
 
Mode::Mode() : nnpType                   (NNPType::HDNNP_2G),
               normalize                 (false            ),
               checkExtrapolationWarnings(false            ),
               numElements               (0                ),
               maxCutoffRadius           (0.0              ),
               cutoffAlpha               (0.0              ),
               meanEnergy                (0.0              ),
               convEnergy                (1.0              ),
               convLength                (1.0              ),
               convCharge                (1.0              ),
               fourPiEps                 (1.0              ),
               ewaldSetup                {},
               kspaceGrid                {}
{
}
 
void Mode::initialize()
{
    log << "\n";
    log << "*****************************************"
           "**************************************\n";
    log << "\n";
    log << "WELCOME TO n²p², A SOFTWARE PACKAGE FOR NEURAL NETWORK "
           "POTENTIALS!\n";
    log << "-------------------------------------------------------"
           "-----------\n";
    log << "\n";
    log << "n²p² version  (from git): " N2P2_GIT_VERSION "\n";
    log << "             (version.h): " N2P2_VERSION "\n";
    log << "------------------------------------------------------------\n";
    log << "Git branch              : " N2P2_GIT_BRANCH "\n";
    log << "Git revision            : " N2P2_GIT_REV "\n";
    log << "Compile date/time       : " __DATE__ " " __TIME__ "\n";
    log << "------------------------------------------------------------\n";
    log << "\n";
    log << "Features/Flags:\n";
    log << "------------------------------------------------------------\n";
    log << "Symmetry function groups     : ";
#ifndef N2P2_NO_SF_GROUPS
    log << "enabled\n";
#else
    log << "disabled\n";
#endif
    log << "Symmetry function cache      : ";
#ifndef N2P2_NO_SF_CACHE
    log << "enabled\n";
#else
    log << "disabled\n";
#endif
    log << "Timing function available    : ";
#ifndef N2P2_NO_TIME
    log << "available\n";
#else
    log << "disabled\n";
#endif
    log << "Asymmetric polynomial SFs    : ";
#ifndef N2P2_NO_ASYM_POLY
    log << "available\n";
#else
    log << "disabled\n";
#endif
    log << "SF low neighbor number check : ";
#ifndef N2P2_NO_NEIGH_CHECK
    log << "enabled\n";
#else
    log << "disabled\n";
#endif
    log << "SF derivative memory layout  : ";
#ifndef N2P2_FULL_SFD_MEMORY
    log << "reduced\n";
#else
    log << "full\n";
#endif
    log << "MPI explicitly disabled      : ";
#ifndef N2P2_NO_MPI
    log << "no\n";
#else
    log << "yes\n";
#endif
#ifdef _OPENMP
    log << strpr("OpenMP threads               : %d\n", omp_get_max_threads());
#endif
    log << "------------------------------------------------------------\n";
    log << "\n";
 
    log << "Please cite the following papers when publishing results "
           "obtained with n²p²:\n";
    log << "-----------------------------------------"
           "--------------------------------------\n";
    log << " * General citation for n²p² and the LAMMPS interface:\n";
    log << "\n";
    log << " Singraber, A.; Behler, J.; Dellago, C.\n";
    log << " Library-Based LAMMPS Implementation of High-Dimensional\n";
    log << " Neural Network Potentials.\n";
    log << " J. Chem. Theory Comput. 2019 15 (3), 1827–1840.\n";
    log << " https://doi.org/10.1021/acs.jctc.8b00770\n";
    log << "-----------------------------------------"
           "--------------------------------------\n";
    log << " * Additionally, if you use the NNP training features of n²p²:\n";
    log << "\n";
    log << " Singraber, A.; Morawietz, T.; Behler, J.; Dellago, C.\n";
    log << " Parallel Multistream Training of High-Dimensional Neural\n";
    log << " Network Potentials.\n";
    log << " J. Chem. Theory Comput. 2019, 15 (5), 3075–3092.\n";
    log << " https://doi.org/10.1021/acs.jctc.8b01092\n";
    log << "-----------------------------------------"
           "--------------------------------------\n";
    log << " * Additionally, if polynomial symmetry functions are used:\n";
    log << "\n";
    log << " Bircher, M. P.; Singraber, A.; Dellago, C.\n";
    log << " Improved Description of Atomic Environments Using Low-Cost\n";
    log << " Polynomial Functions with Compact Support.\n";
    log << " arXiv:2010.14414 [cond-mat, physics:physics] 2020.\n";
    log << " https://arxiv.org/abs/2010.14414\n";
 
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::loadSettingsFile(string const& fileName)
{
    log << "\n";
    log << "*** SETUP: SETTINGS FILE ****************"
           "**************************************\n";
    log << "\n";
 
    size_t numCriticalProblems = settings.loadFile(fileName);
    log << settings.info();
    if (numCriticalProblems > 0)
    {
        throw runtime_error(strpr("ERROR: %zu critical problem(s) were found "
                                  "in settings file.\n", numCriticalProblems));
    }
 
    if (settings.keywordExists("nnp_type"))
    {
        string nnpTypeString = settings["nnp_type"];
        if      (nnpTypeString == "2G-HDNNP") nnpType = NNPType::HDNNP_2G;
        else if (nnpTypeString == "4G-HDNNP") nnpType = NNPType::HDNNP_4G;
        else if (nnpTypeString == "Q-HDNNP")  nnpType = NNPType::HDNNP_Q;
        else nnpType = (NNPType)atoi(settings["nnp_type"].c_str());
    }
 
    if (nnpType == NNPType::HDNNP_2G)
    {
        log << "This settings file defines a short-range NNP (2G-HDNNP).\n";
    }
    else if (nnpType == NNPType::HDNNP_4G)
    {
        log << "This settings file defines a NNP with electrostatics and\n"
               "non-local charge transfer (4G-HDNNP).\n";
    }
    else if (nnpType == NNPType::HDNNP_Q)
    {
        log << "This settings file defines a short-range NNP similar to\n"
               "4G-HDNNP with additional charge NN but with neither\n"
               "electrostatics nor global charge equilibration\n"
               "(method by M. Bircher).\n";
    }
    else
    {
        throw runtime_error("ERROR: Unknown NNP type.\n");
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupGeneric(string const& nnpDir,
                        bool skipNormalize,
                        bool initialHardness)
{
    if (!skipNormalize) setupNormalization();
    setupElementMap();
    setupElements();
    if (nnpType == NNPType::HDNNP_4G)
        setupElectrostatics(initialHardness, nnpDir);
    setupCutoff();
    setupSymmetryFunctions();
    setupCutoffMatrix();
#ifndef N2P2_FULL_SFD_MEMORY
    setupSymmetryFunctionMemory(false);
#endif
#ifndef N2P2_NO_SF_CACHE
    setupSymmetryFunctionCache();
#endif
#ifndef N2P2_NO_SF_GROUPS
    setupSymmetryFunctionGroups();
#endif
    setupNeuralNetwork();
 
    return;
}
 
void Mode::setupNormalization(bool standalone)
{
    if (standalone)
    {
    log << "\n";
    log << "*** SETUP: NORMALIZATION ****************"
           "**************************************\n";
    log << "\n";
    }
 
    if (settings.keywordExists("mean_energy") &&
        settings.keywordExists("conv_energy") &&
        settings.keywordExists("conv_length"))
    {
        normalize = true;
        meanEnergy = atof(settings["mean_energy"].c_str());
        convEnergy = atof(settings["conv_energy"].c_str());
        convLength = atof(settings["conv_length"].c_str());
 
        log << "Data set normalization is used.\n";
        log << strpr("Mean energy per atom     : %24.16E\n", meanEnergy);
        log << strpr("Conversion factor energy : %24.16E\n", convEnergy);
        log << strpr("Conversion factor length : %24.16E\n", convLength);
        if (settings.keywordExists("conv_charge"))
        {
            convCharge = atof(settings["conv_charge"].c_str());
            log << strpr("Conversion factor charge : %24.16E\n",
                         convCharge);
        }
        if (settings.keywordExists("atom_energy"))
        {
            log << "\n";
            log << "Atomic energy offsets are used in addition to"
                   " data set normalization.\n";
            log << "Offsets will be subtracted from reference energies BEFORE"
                   " normalization is applied.\n";
        }
    }
    else if ((!settings.keywordExists("mean_energy")) &&
             (!settings.keywordExists("conv_energy")) &&
             (!settings.keywordExists("conv_length")) &&
             (!settings.keywordExists("conv_charge")))
    {
        normalize = false;
        log << "Data set normalization is not used.\n";
    }
    else
    {
        throw runtime_error("ERROR: Incorrect usage of normalization"
                            " keywords.\n"
                            "       Use all or none of \"mean_energy\", \"conv_energy\""
                            " and \"conv_length\".\n");
    }
 
    if (standalone)
    {
    log << "*****************************************"
           "**************************************\n";
    }
 
    return;
}
 
void Mode::setupElementMap()
{
    log << "\n";
    log << "*** SETUP: ELEMENT MAP ******************"
           "**************************************\n";
    log << "\n";
 
    elementMap.registerElements(settings["elements"]);
    log << strpr("Number of element strings found: %d\n", elementMap.size());
    for (size_t i = 0; i < elementMap.size(); ++i)
    {
        log << strpr("Element %2zu: %2s (%3zu)\n", i, elementMap[i].c_str(),
                     elementMap.atomicNumber(i));
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupElements()
{
    log << "\n";
    log << "*** SETUP: ELEMENTS *********************"
           "**************************************\n";
    log << "\n";
 
    numElements = (size_t)atoi(settings["number_of_elements"].c_str());
    if (numElements != elementMap.size())
    {
        throw runtime_error("ERROR: Inconsistent number of elements.\n");
    }
    log << strpr("Number of elements is consistent: %zu\n", numElements);
 
    for (size_t i = 0; i < numElements; ++i)
    {
        elements.push_back(Element(i, elementMap));
    }
 
    if (settings.keywordExists("atom_energy"))
    {
        settings::Settings::KeyRange r = settings.getValues("atom_energy");
        for (settings::Settings::KeyMap::const_iterator it = r.first;
             it != r.second; ++it)
        {
            vector<string> args    = split(reduce(it->second.first));
            size_t         element = elementMap[args.at(0)];
            elements.at(element).
                setAtomicEnergyOffset(atof(args.at(1).c_str()));
        }
    }
    log << "Atomic energy offsets per element:\n";
    for (size_t i = 0; i < elementMap.size(); ++i)
    {
        log << strpr("Element %2zu: %16.8E\n",
                     i, elements.at(i).getAtomicEnergyOffset());
    }
    log << "Energy offsets are automatically subtracted from reference "
           "energies.\n";
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupElectrostatics(bool   initialHardness,
                               string directoryPrefix,
                               string fileNameFormat)
{
    log << "\n";
    log << "*** SETUP: ELECTROSTATICS ***************"
           "**************************************\n";
    log << "\n";
 
    // Atomic hardness.
    if (initialHardness)
    {
        settings::Settings::KeyRange r = settings.getValues("initial_hardness");
        for (settings::Settings::KeyMap::const_iterator it = r.first;
             it != r.second; ++it)
        {
            vector<string> args    = split(reduce(it->second.first));
            size_t         element = elementMap[args.at(0)];
            double hardness = atof(args.at(1).c_str());
            if (normalize) hardness = normalized("hardness", hardness);
            elements.at(element).setHardness(hardness);
        }
        for (size_t i = 0; i < numElements; ++i)
        {
            double hardness = elements.at(i).getHardness();
            if (normalize) hardness = physical("hardness", hardness);
            log << strpr("Initial atomic hardness for element %2s: %16.8E\n",
                         elements.at(i).getSymbol().c_str(),
                         hardness);
        }
    }
    else
    {
        string actualFileNameFormat = directoryPrefix + fileNameFormat;
        log << strpr("Atomic hardness file name format: %s\n",
                     actualFileNameFormat.c_str());
        for (size_t i = 0; i < numElements; ++i)
        {
            string fileName = strpr(actualFileNameFormat.c_str(),
                                    elements.at(i).getAtomicNumber());
            log << strpr("Atomic hardness for element %2s from file %s: ",
                         elements.at(i).getSymbol().c_str(),
                         fileName.c_str());
            vector<double> const data = readColumnsFromFile(fileName,
                                                            {0}).at(0);
            if (data.size() != 1)
            {
                throw runtime_error("ERROR: Atomic hardness data is "
                                    "inconsistent.\n");
            }
            double hardness = data.at(0);
            if (normalize) hardness = normalized("hardness", hardness);
            elements.at(i).setHardness(hardness);
            if (normalize) hardness = physical("hardness", hardness);
            log << strpr("%16.8E\n", hardness);
        }
    }
    log << "\n";
 
    // Gaussian width of charges.
    double maxQsigma = 0.0;
    settings::Settings::KeyRange r = settings.getValues("fixed_gausswidth");
    for (settings::Settings::KeyMap::const_iterator it = r.first;
         it != r.second; ++it)
    {
        vector<string> args    = split(reduce(it->second.first));
        size_t         element = elementMap[args.at(0)];
        double qsigma = atof(args.at(1).c_str());
        if (normalize) qsigma *= convLength;
        elements.at(element).setQsigma(qsigma);
 
        maxQsigma = max(qsigma, maxQsigma);
    }
    log << "Gaussian width of charge distribution per element:\n";
    for (size_t i = 0; i < elementMap.size(); ++i)
    {
        double qsigma = elements.at(i).getQsigma();
        if (normalize) qsigma /= convLength;
        log << strpr("Element %2zu: %16.8E\n",
                     i, qsigma);
    }
    log << "\n";
 
    // K-Space Solver
    if (settings.keywordExists("kspace_solver"))
    {
        string kspace_solver_string = settings["kspace_solver"];
        if (kspace_solver_string == "ewald")
        {
            kspaceGrid.kspaceSolver = KSPACESolver::EWALD_SUM;
        }
        else if (kspace_solver_string == "pppm")
        {
            kspaceGrid.kspaceSolver = KSPACESolver::PPPM;
        }
        else if (kspace_solver_string == "ewald_lammps")
        {
            kspaceGrid.kspaceSolver = KSPACESolver::EWALD_SUM_LAMMPS;
        }
    }
    else kspaceGrid.kspaceSolver = KSPACESolver::EWALD_SUM;
 
    // Ewald truncation error method.
    if (settings.keywordExists("ewald_truncation_error_method"))
    {
        string truncation_method_string =
            settings["ewald_truncation_error_method"];
        if (truncation_method_string == "0")
        {
            ewaldSetup.setTruncMethod(EWALDTruncMethod::JACKSON_CATLOW);
        }
        else if (truncation_method_string == "1")
        {
            ewaldSetup.setTruncMethod(EWALDTruncMethod::KOLAFA_PERRAM);
        }
    }
    else ewaldSetup.setTruncMethod(EWALDTruncMethod::JACKSON_CATLOW);
 
    // Ewald precision.
    if (settings.keywordExists("ewald_prec"))
    {
        vector<string> args = split(settings["ewald_prec"]);
        ewaldSetup.readFromArgs(args);
        ewaldSetup.setMaxQSigma(maxQsigma);
        if (normalize) ewaldSetup.toNormalizedUnits(convEnergy, convLength);
    }
    else if (ewaldSetup.getTruncMethod() == EWALDTruncMethod::KOLAFA_PERRAM)
    {
        throw runtime_error("ERROR: ewald_truncation_error_method 1 requires "
                            "ewald_prec.");
    }
    log << strpr("Ewald truncation error method: %16d\n",
                 ewaldSetup.getTruncMethod());
    log << strpr("Ewald precision:               %16.8E\n",
                 ewaldSetup.getPrecision());
    if (ewaldSetup.getTruncMethod() == EWALDTruncMethod::KOLAFA_PERRAM)
    {
        log << strpr("Ewald expected maximum charge: %16.8E\n",
                     ewaldSetup.getMaxCharge());
    }
    log << "\n";
 
    // 4 * pi * epsilon
    if (settings.keywordExists("four_pi_epsilon"))
        fourPiEps = atof(settings["four_pi_epsilon"].c_str());
    if (normalize)
        fourPiEps *= pow(convCharge, 2) / (convLength * convEnergy);
 
    // Screening function.
    if (settings.keywordExists("screen_electrostatics"))
    {
        vector<string> args = split(settings["screen_electrostatics"]);
        double inner = atof(args.at(0).c_str());
        double outer = atof(args.at(1).c_str());
        string type = "c"; // Default core function is cosine.
        if (args.size() > 2) type = args.at(2); 
        screeningFunction.setInnerOuter(inner, outer);
        screeningFunction.setCoreFunction(type);
        for (auto s : screeningFunction.info()) log << s;
        if (normalize) screeningFunction.changeLengthUnits(convLength);
    }
    else
    {
        // Set screening function in such way that it will always be 1.0.
        // This may not be very efficient, may optimize later.
        screeningFunction.setInnerOuter(-2.0, -1.0);
        log << "Screening function not used.\n";
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupCutoff()
{
    log << "\n";
    log << "*** SETUP: CUTOFF FUNCTIONS *************"
           "**************************************\n";
    log << "\n";
 
    vector<string> args = split(settings["cutoff_type"]);
 
    cutoffType = (CutoffFunction::CutoffType) atoi(args.at(0).c_str());
    if (args.size() > 1)
    {
        cutoffAlpha = atof(args.at(1).c_str());
        if (0.0 < cutoffAlpha && cutoffAlpha >= 1.0)
        {
            throw invalid_argument("ERROR: 0 <= alpha < 1.0 is required.\n");
        }
    }
    log << strpr("Parameter alpha for inner cutoff: %f\n", cutoffAlpha);
    log << "Inner cutoff = Symmetry function cutoff * alpha\n";
 
    log << "Equal cutoff function type for all symmetry functions:\n";
    if (cutoffType == CutoffFunction::CT_COS)
    {
        log << strpr("CutoffFunction::CT_COS (%d)\n", cutoffType);
        log << "x := (r - rc * alpha) / (rc - rc * alpha)\n";
        log << "f(x) = 1/2 * (cos(pi*x) + 1)\n";
    }
    else if (cutoffType == CutoffFunction::CT_TANHU)
    {
        log << strpr("CutoffFunction::CT_TANHU (%d)\n", cutoffType);
        log << "f(r) = tanh^3(1 - r/rc)\n";
        if (cutoffAlpha > 0.0)
        {
            log << "WARNING: Inner cutoff parameter not used in combination"
                   " with this cutoff function.\n";
        }
    }
    else if (cutoffType == CutoffFunction::CT_TANH)
    {
        log << strpr("CutoffFunction::CT_TANH (%d)\n", cutoffType);
        log << "f(r) = c * tanh^3(1 - r/rc), f(0) = 1\n";
        if (cutoffAlpha > 0.0)
        {
            log << "WARNING: Inner cutoff parameter not used in combination"
                   " with this cutoff function.\n";
        }
    }
    else if (cutoffType == CutoffFunction::CT_POLY1)
    {
        log << strpr("CutoffFunction::CT_POLY1 (%d)\n", cutoffType);
        log << "x := (r - rc * alpha) / (rc - rc * alpha)\n";
        log << "f(x) = (2x - 3)x^2 + 1\n";
    }
    else if (cutoffType == CutoffFunction::CT_POLY2)
    {
        log << strpr("CutoffFunction::CT_POLY2 (%d)\n", cutoffType);
        log << "x := (r - rc * alpha) / (rc - rc * alpha)\n";
        log << "f(x) = ((15 - 6x)x - 10)x^3 + 1\n";
    }
    else if (cutoffType == CutoffFunction::CT_POLY3)
    {
        log << strpr("CutoffFunction::CT_POLY3 (%d)\n", cutoffType);
        log << "x := (r - rc * alpha) / (rc - rc * alpha)\n";
        log << "f(x) = (x(x(20x - 70) + 84) - 35)x^4 + 1\n";
    }
    else if (cutoffType == CutoffFunction::CT_POLY4)
    {
        log << strpr("CutoffFunction::CT_POLY4 (%d)\n", cutoffType);
        log << "x := (r - rc * alpha) / (rc - rc * alpha)\n";
        log << "f(x) = (x(x((315 - 70x)x - 540) + 420) - 126)x^5 + 1\n";
    }
    else if (cutoffType == CutoffFunction::CT_EXP)
    {
        log << strpr("CutoffFunction::CT_EXP (%d)\n", cutoffType);
        log << "x := (r - rc * alpha) / (rc - rc * alpha)\n";
        log << "f(x) = exp(-1 / 1 - x^2)\n";
    }
    else if (cutoffType == CutoffFunction::CT_HARD)
    {
        log << strpr("CutoffFunction::CT_HARD (%d)\n", cutoffType);
        log << "f(r) = 1\n";
        log << "WARNING: Hard cutoff used!\n";
    }
    else
    {
        throw invalid_argument("ERROR: Unknown cutoff type.\n");
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupSymmetryFunctions()
{
    log << "\n";
    log << "*** SETUP: SYMMETRY FUNCTIONS ***********"
           "**************************************\n";
    log << "\n";
 
    settings::Settings::KeyRange r = settings.getValues("symfunction_short");
    for (settings::Settings::KeyMap::const_iterator it = r.first; it != r.second; ++it)
    {
        vector<string> args    = split(reduce(it->second.first));
        size_t         element = elementMap[args.at(0)];
 
        elements.at(element).addSymmetryFunction(it->second.first,
                                                 it->second.second);
    }
 
    log << "Abbreviations:\n";
    log << "--------------\n";
    log << "ind .... Symmetry function index.\n";
    log << "ec ..... Central atom element.\n";
    log << "tp ..... Symmetry function type.\n";
    log << "sbtp ... Symmetry function subtype (e.g. cutoff type).\n";
    log << "e1 ..... Neighbor 1 element.\n";
    log << "e2 ..... Neighbor 2 element.\n";
    log << "eta .... Gaussian width eta.\n";
    log << "rs/rl... Shift distance of Gaussian or left cutoff radius "
           "for polynomial.\n";
    log << "angl.... Left cutoff angle for polynomial.\n";
    log << "angr.... Right cutoff angle for polynomial.\n";
    log << "la ..... Angle prefactor lambda.\n";
    log << "zeta ... Angle term exponent zeta.\n";
    log << "rc ..... Cutoff radius / right cutoff radius for polynomial.\n";
    log << "a ...... Free parameter alpha (e.g. cutoff alpha).\n";
    log << "ln ..... Line number in settings file.\n";
    log << "\n";
    maxCutoffRadius = 0.0;
    for (vector<Element>::iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        if (normalize) it->changeLengthUnitSymmetryFunctions(convLength);
        it->sortSymmetryFunctions();
        maxCutoffRadius = max(it->getMaxCutoffRadius(), maxCutoffRadius);
        it->setCutoffFunction(cutoffType, cutoffAlpha);
        log << strpr("Short range atomic symmetry functions element %2s :\n",
                     it->getSymbol().c_str());
        log << "--------------------------------------------------"
               "-----------------------------------------------\n";
        log << " ind ec tp sbtp e1 e2       eta      rs/rl         "
               "rc   angl   angr la zeta    a    ln\n";
        log << "--------------------------------------------------"
               "-----------------------------------------------\n";
        log << it->infoSymmetryFunctionParameters();
        log << "--------------------------------------------------"
               "-----------------------------------------------\n";
    }
    minNeighbors.clear();
    minNeighbors.resize(numElements, 0);
    minCutoffRadius.clear();
    minCutoffRadius.resize(numElements, maxCutoffRadius);
    for (size_t i = 0; i < numElements; ++i)
    {
        minNeighbors.at(i) = elements.at(i).getMinNeighbors();
        minCutoffRadius.at(i) = elements.at(i).getMinCutoffRadius();
        log << strpr("Minimum cutoff radius for element %2s: %f\n",
                     elements.at(i).getSymbol().c_str(),
                     minCutoffRadius.at(i) / convLength);
    }
    log << strpr("Maximum cutoff radius (global)      : %f\n",
                 maxCutoffRadius / convLength);
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupSymmetryFunctionScalingNone()
{
    log << "\n";
    log << "*** SETUP: SYMMETRY FUNCTION SCALING ****"
           "**************************************\n";
    log << "\n";
 
    log << "No scaling for symmetry functions.\n";
    for (vector<Element>::iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        it->setScalingNone();
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupSymmetryFunctionScaling(string const& fileName)
{
    log << "\n";
    log << "*** SETUP: SYMMETRY FUNCTION SCALING ****"
           "**************************************\n";
    log << "\n";
 
    log << "Equal scaling type for all symmetry functions:\n";
    if (   ( settings.keywordExists("scale_symmetry_functions" ))
        && (!settings.keywordExists("center_symmetry_functions")))
    {
        scalingType = SymFnc::ST_SCALE;
        log << strpr("Scaling type::ST_SCALE (%d)\n", scalingType);
        log << "Gs = Smin + (Smax - Smin) * (G - Gmin) / (Gmax - Gmin)\n";
    }
    else if (   (!settings.keywordExists("scale_symmetry_functions" ))
             && ( settings.keywordExists("center_symmetry_functions")))
    {
        scalingType = SymFnc::ST_CENTER;
        log << strpr("Scaling type::ST_CENTER (%d)\n", scalingType);
        log << "Gs = G - Gmean\n";
    }
    else if (   ( settings.keywordExists("scale_symmetry_functions" ))
             && ( settings.keywordExists("center_symmetry_functions")))
    {
        scalingType = SymFnc::ST_SCALECENTER;
        log << strpr("Scaling type::ST_SCALECENTER (%d)\n", scalingType);
        log << "Gs = Smin + (Smax - Smin) * (G - Gmean) / (Gmax - Gmin)\n";
    }
    else if (settings.keywordExists("scale_symmetry_functions_sigma"))
    {
        scalingType = SymFnc::ST_SCALESIGMA;
        log << strpr("Scaling type::ST_SCALESIGMA (%d)\n", scalingType);
        log << "Gs = Smin + (Smax - Smin) * (G - Gmean) / Gsigma\n";
    }
    else
    {
        scalingType = SymFnc::ST_NONE;
        log << strpr("Scaling type::ST_NONE (%d)\n", scalingType);
        log << "Gs = G\n";
        log << "WARNING: No symmetry function scaling!\n";
    }
 
    double Smin = 0.0;
    double Smax = 0.0;
    if (scalingType == SymFnc::ST_SCALE ||
        scalingType == SymFnc::ST_SCALECENTER ||
        scalingType == SymFnc::ST_SCALESIGMA)
    {
        if (settings.keywordExists("scale_min_short"))
        {
            Smin = atof(settings["scale_min_short"].c_str());
        }
        else
        {
            log << "WARNING: Keyword \"scale_min_short\" not found.\n";
            log << "         Default value for Smin = 0.0.\n";
            Smin = 0.0;
        }
 
        if (settings.keywordExists("scale_max_short"))
        {
            Smax = atof(settings["scale_max_short"].c_str());
        }
        else
        {
            log << "WARNING: Keyword \"scale_max_short\" not found.\n";
            log << "         Default value for Smax = 1.0.\n";
            Smax = 1.0;
        }
 
        log << strpr("Smin = %f\n", Smin);
        log << strpr("Smax = %f\n", Smax);
    }
 
    log << strpr("Symmetry function scaling statistics from file: %s\n",
                 fileName.c_str());
    log << "-----------------------------------------"
           "--------------------------------------\n";
    ifstream file;
    file.open(fileName.c_str());
    if (!file.is_open())
    {
        throw runtime_error("ERROR: Could not open file: \"" + fileName
                            + "\".\n");
    }
    string line;
    vector<string> lines;
    while (getline(file, line))
    {
        if (line.at(0) != '#') lines.push_back(line);
    }
    file.close();
 
    log << "\n";
    log << "Abbreviations:\n";
    log << "--------------\n";
    log << "ind ..... Symmetry function index.\n";
    log << "min ..... Minimum symmetry function value.\n";
    log << "max ..... Maximum symmetry function value.\n";
    log << "mean .... Mean symmetry function value.\n";
    log << "sigma ... Standard deviation of symmetry function values.\n";
    log << "sf ...... Scaling factor for derivatives.\n";
    log << "Smin .... Desired minimum scaled symmetry function value.\n";
    log << "Smax .... Desired maximum scaled symmetry function value.\n";
    log << "t ....... Scaling type.\n";
    log << "\n";
    for (vector<Element>::iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        it->setScaling(scalingType, lines, Smin, Smax);
        log << strpr("Scaling data for symmetry functions element %2s :\n",
                     it->getSymbol().c_str());
        log << "-----------------------------------------"
               "--------------------------------------\n";
        log << " ind       min       max      mean     sigma        sf  Smin  Smax t\n";
        log << "-----------------------------------------"
               "--------------------------------------\n";
        log << it->infoSymmetryFunctionScaling();
        log << "-----------------------------------------"
               "--------------------------------------\n";
        lines.erase(lines.begin(), lines.begin() + it->numSymmetryFunctions());
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupSymmetryFunctionGroups()
{
    log << "\n";
    log << "*** SETUP: SYMMETRY FUNCTION GROUPS *****"
           "**************************************\n";
    log << "\n";
 
    log << "Abbreviations:\n";
    log << "--------------\n";
    log << "ind .... Symmetry function index.\n";
    log << "ec ..... Central atom element.\n";
    log << "tp ..... Symmetry function type.\n";
    log << "sbtp ... Symmetry function subtype (e.g. cutoff type).\n";
    log << "e1 ..... Neighbor 1 element.\n";
    log << "e2 ..... Neighbor 2 element.\n";
    log << "eta .... Gaussian width eta.\n";
    log << "rs/rl... Shift distance of Gaussian or left cutoff radius "
           "for polynomial.\n";
    log << "angl.... Left cutoff angle for polynomial.\n";
    log << "angr.... Right cutoff angle for polynomial.\n";
    log << "la ..... Angle prefactor lambda.\n";
    log << "zeta ... Angle term exponent zeta.\n";
    log << "rc ..... Cutoff radius / right cutoff radius for polynomial.\n";
    log << "a ...... Free parameter alpha (e.g. cutoff alpha).\n";
    log << "ln ..... Line number in settings file.\n";
    log << "mi ..... Member index.\n";
    log << "sfi .... Symmetry function index.\n";
    log << "e ...... Recalculate exponential term.\n";
    log << "\n";
    for (vector<Element>::iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        it->setupSymmetryFunctionGroups();
        log << strpr("Short range atomic symmetry function groups "
                     "element %2s :\n", it->getSymbol().c_str());
        log << "------------------------------------------------------"
               "----------------------------------------------------\n";
        log << " ind ec tp sbtp e1 e2       eta      rs/rl         "
               "rc   angl   angr la zeta    a    ln   mi  sfi e\n";
        log << "------------------------------------------------------"
               "----------------------------------------------------\n";
        log << it->infoSymmetryFunctionGroups();
        log << "------------------------------------------------------"
               "----------------------------------------------------\n";
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupSymmetryFunctionMemory(bool verbose)
{
    log << "\n";
    log << "*** SETUP: SYMMETRY FUNCTION MEMORY *****"
           "**************************************\n";
    log << "\n";
 
    for (auto& e : elements)
    {
        e.setupSymmetryFunctionMemory();
        vector<size_t> symmetryFunctionNumTable
            = e.getSymmetryFunctionNumTable();
        vector<vector<size_t>> symmetryFunctionTable
            = e.getSymmetryFunctionTable();
        log << strpr("Symmetry function derivatives memory table "
                     "for element %2s :\n", e.getSymbol().c_str());
        log << "-----------------------------------------"
               "--------------------------------------\n";
        log << "Relevant symmetry functions for neighbors with element:\n";
        for (size_t i = 0; i < numElements; ++i)
        {
            log << strpr("- %2s: %4zu of %4zu (%5.1f %)\n",
                         elementMap[i].c_str(),
                         symmetryFunctionNumTable.at(i),
                         e.numSymmetryFunctions(),
                         (100.0 * symmetryFunctionNumTable.at(i))
                         / e.numSymmetryFunctions());
            if (verbose)
            {
                log << "-----------------------------------------"
                       "--------------------------------------\n";
                for (auto isf : symmetryFunctionTable.at(i))
                {
                    SymFnc const& sf = e.getSymmetryFunction(isf);
                    log << sf.parameterLine();
                }
                log << "-----------------------------------------"
                       "--------------------------------------\n";
            }
        }
        log << "-----------------------------------------"
               "--------------------------------------\n";
    }
    if (verbose)
    {
        for (auto& e : elements)
        {
            log << strpr("%2s - symmetry function per-element index table:\n",
                         e.getSymbol().c_str());
            log << "-----------------------------------------"
                   "--------------------------------------\n";
            log << " ind";
            for (size_t i = 0; i < numElements; ++i)
            {
                log << strpr(" %4s", elementMap[i].c_str());
            }
            log << "\n";
            log << "-----------------------------------------"
                   "--------------------------------------\n";
            for (size_t i = 0; i < e.numSymmetryFunctions(); ++i)
            {
                SymFnc const& sf = e.getSymmetryFunction(i);
                log << strpr("%4zu", sf.getIndex() + 1);
                vector<size_t> indexPerElement = sf.getIndexPerElement();
                for (auto ipe : sf.getIndexPerElement())
                {
                    if (ipe == numeric_limits<size_t>::max())
                    {
                        log << strpr("     ");
                    }
                    else
                    {
                        log << strpr(" %4zu", ipe + 1);
                    }
                }
                log << "\n";
            }
            log << "-----------------------------------------"
                   "--------------------------------------\n";
        }
 
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
#ifndef N2P2_NO_SF_CACHE
void Mode::setupSymmetryFunctionCache(bool verbose)
{
    log << "\n";
    log << "*** SETUP: SYMMETRY FUNCTION CACHE ******"
           "**************************************\n";
    log << "\n";
 
    for (size_t i = 0; i < numElements; ++i)
    {
        using SFCacheList = Element::SFCacheList;
        vector<vector<SFCacheList>> cacheLists(numElements);
        Element& e = elements.at(i);
        for (size_t j = 0; j < e.numSymmetryFunctions(); ++j)
        {
            SymFnc const& s = e.getSymmetryFunction(j);
            for (auto identifier : s.getCacheIdentifiers())
            {
                size_t ne = atoi(split(identifier)[0].c_str());
                bool unknown = true;
                for (auto& c : cacheLists.at(ne))
                {
                    if (identifier == c.identifier)
                    {
                        c.indices.push_back(s.getIndex());
                        unknown = false;
                        break;
                    }
                }
                if (unknown)
                {
                    cacheLists.at(ne).push_back(SFCacheList());
                    cacheLists.at(ne).back().element = ne;
                    cacheLists.at(ne).back().identifier = identifier;
                    cacheLists.at(ne).back().indices.push_back(s.getIndex());
                }
            }
        }
        if (verbose)
        {
            log << strpr("Multiple cache identifiers for element %2s:\n\n",
                         e.getSymbol().c_str());
        }
        double cacheUsageMean = 0.0;
        size_t cacheCount = 0;
        for (size_t j = 0; j < numElements; ++j)
        {
            if (verbose)
            {
                log << strpr("Neighbor %2s:\n", elementMap[j].c_str());
            }
            vector<SFCacheList>& c = cacheLists.at(j);
            c.erase(remove_if(c.begin(),
                              c.end(),
                              [](SFCacheList l)
                              {
                                  return l.indices.size() <= 1;
                              }), c.end());
            cacheCount += c.size();
            for (size_t k = 0; k < c.size(); ++k)
            {
                cacheUsageMean += c.at(k).indices.size();
                if (verbose)
                {
                    log << strpr("Cache %zu, Identifier \"%s\", "
                                 "Symmetry functions",
                                 k, c.at(k).identifier.c_str());
                    for (auto si : c.at(k).indices)
                    {
                        log << strpr(" %zu", si);
                    }
                    log << "\n";
                }
            }
        }
        e.setCacheIndices(cacheLists);
        //for (size_t j = 0; j < e.numSymmetryFunctions(); ++j)
        //{
        //    SymFnc const& sf = e.getSymmetryFunction(j);
        //    auto indices = sf.getCacheIndices();
        //    size_t count = 0;
        //    for (size_t k = 0; k < numElements; ++k)
        //    {
        //        count += indices.at(k).size();
        //    }
        //    if (count > 0)
        //    {
        //        log << strpr("SF %4zu:\n", sf.getIndex());
        //    }
        //    for (size_t k = 0; k < numElements; ++k)
        //    {
        //        if (indices.at(k).size() > 0)
        //        {
        //            log << strpr("- Neighbor %2s:", elementMap[k].c_str());
        //            for (size_t l = 0; l < indices.at(k).size(); ++l)
        //            {
        //                log << strpr(" %zu", indices.at(k).at(l));
        //            }
        //            log << "\n";
        //        }
        //    }
        //}
        cacheUsageMean /= cacheCount;
        log << strpr("Element %2s: in total %zu caches, "
                     "used %3.2f times on average.\n",
                     e.getSymbol().c_str(), cacheCount, cacheUsageMean);
        if (verbose)
        {
            log << "-----------------------------------------"
                   "--------------------------------------\n";
        }
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
#endif
 
void Mode::setupSymmetryFunctionStatistics(bool collectStatistics,
                                           bool collectExtrapolationWarnings,
                                           bool writeExtrapolationWarnings,
                                           bool stopOnExtrapolationWarnings)
{
    log << "\n";
    log << "*** SETUP: SYMMETRY FUNCTION STATISTICS *"
           "**************************************\n";
    log << "\n";
 
    log << "Equal symmetry function statistics for all elements.\n";
    log << strpr("Collect min/max/mean/sigma                        : %d\n",
                 (int)collectStatistics);
    log << strpr("Collect extrapolation warnings                    : %d\n",
                 (int)collectExtrapolationWarnings);
    log << strpr("Write extrapolation warnings immediately to stderr: %d\n",
                 (int)writeExtrapolationWarnings);
    log << strpr("Halt on any extrapolation warning                 : %d\n",
                 (int)stopOnExtrapolationWarnings);
    for (vector<Element>::iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        it->statistics.collectStatistics = collectStatistics;
        it->statistics.collectExtrapolationWarnings =
                                                  collectExtrapolationWarnings;
        it->statistics.writeExtrapolationWarnings = writeExtrapolationWarnings;
        it->statistics.stopOnExtrapolationWarnings =
                                                   stopOnExtrapolationWarnings;
    }
 
    checkExtrapolationWarnings = collectStatistics
                              || collectExtrapolationWarnings
                              || writeExtrapolationWarnings
                              || stopOnExtrapolationWarnings;
 
    log << "*****************************************"
           "**************************************\n";
    return;
}
 
void Mode::setupCutoffMatrix()
{
    double rCutScreen = screeningFunction.getOuter();
    cutoffs.resize(numElements);
    for(auto const& e : elements)
    {
        e.getCutoffRadii(cutoffs.at(e.getIndex()));
        if ( rCutScreen > 0 &&
             !vectorContains(cutoffs.at(e.getIndex()), rCutScreen) )
        {
            cutoffs.at(e.getIndex()).push_back(rCutScreen);
        }
    }
}
 
void Mode::setupNeuralNetwork()
{
    log << "\n";
    log << "*** SETUP: NEURAL NETWORKS **************"
           "**************************************\n";
    log << "\n";
 
    string id;
 
    // Some NNP types require extra NNs.
    if (nnpType == NNPType::HDNNP_4G)
    {
        id = "elec";
        nnk.push_back(id);
        nns[id].id = id;
        nns.at(id).name = "electronegativity";
        nns.at(id).weightFileFormat = "weightse.%03zu.data";
        nns.at(id).keywordSuffix = "_electrostatic";
        nns.at(id).keywordSuffix2 = "_charge";
    }
    else if(nnpType == NNPType::HDNNP_Q)
    {
        id = "elec";
        nnk.push_back(id);
        nns[id].id = id;
        nns.at(id).name = "charge";
        nns.at(id).weightFileFormat = "weightse.%03zu.data";
        nns.at(id).keywordSuffix = "_electrostatic";
        nns.at(id).keywordSuffix2 = "_charge";
    }
 
    // All NNP types contain a short range NN.
    id = "short";
    nnk.push_back(id);
    nns[id].id = id;
    nns.at(id).name = "short range";
    nns.at(id).weightFileFormat = "weights.%03zu.data";
    nns.at(id).keywordSuffix = "_short";
    nns.at(id).keywordSuffix2 = "_short";
 
    // Loop over all NNs and set global properties.
    for (auto& k : nnk)
    {
        // Each elements number of hidden layers.
        size_t globalNumHiddenLayers = 0;
        // Abbreviation for current NN.
        NNSetup& nn = nns.at(k);
        // Set size of NN topology vector.
        nn.topology.resize(numElements);
        // First, check for global number of hidden layers.
        string keyword = "global_hidden_layers" + nn.keywordSuffix;
        if (settings.keywordExists(keyword))
        {
            globalNumHiddenLayers = atoi(settings[keyword].c_str());
            for (auto& t : nn.topology)
            {
                t.numLayers = globalNumHiddenLayers + 2;
            }
        }
        // Now, check for per-element number of hidden layers.
        keyword = "element_hidden_layers" + nn.keywordSuffix;
        if (settings.keywordExists(keyword))
        {
            settings::Settings::KeyRange r = settings.getValues(keyword);
            for (settings::Settings::KeyMap::const_iterator it = r.first;
                 it != r.second; ++it)
            {
                vector<string> args = split(reduce(it->second.first));
                size_t const e = elementMap[args.at(0)];
                size_t const n = atoi(args.at(1).c_str());
                nn.topology.at(e).numLayers = n + 2;
            }
        }
        // Check whether user has set all NN's number of layers correctly.
        for (auto& t : nn.topology)
        {
            if (t.numLayers == 0)
            {
                throw runtime_error("ERROR: Number of neural network hidden "
                                    "layers unset for some elements.\n");
            }
        }
        // Finally, allocate NN topologies data.
        for (auto& t : nn.topology)
        {
            t.numNeuronsPerLayer.resize(t.numLayers, 0);
            t.activationFunctionsPerLayer.resize(t.numLayers,
                                                 NeuralNetwork::AF_UNSET);
        }
 
        // Now read global number of neurons and activation functions.
        vector<string> globalNumNeuronsPerHiddenLayer;
        keyword = "global_nodes" + nn.keywordSuffix;
        if (settings.keywordExists(keyword))
        {
            globalNumNeuronsPerHiddenLayer = split(reduce(settings[keyword]));
            if (globalNumHiddenLayers != globalNumNeuronsPerHiddenLayer.size())
            {
                throw runtime_error(strpr("ERROR: Inconsistent global NN "
                                          "topology keyword \"%s\".\n",
                                          keyword.c_str()));
            }
        }
        vector<string> globalActivationFunctions;
        keyword = "global_activation" + nn.keywordSuffix;
        if (settings.keywordExists(keyword))
        {
            globalActivationFunctions = split(reduce(settings[keyword]));
            if (globalNumHiddenLayers != globalActivationFunctions.size() - 1)
            {
                throw runtime_error(strpr("ERROR: Inconsistent global NN "
                                          "topology keyword \"%s\".\n",
                                          keyword.c_str()));
            }
        }
        // Set global number of neurons and activation functions if provided.
        bool globalNumNeurons = (globalNumNeuronsPerHiddenLayer.size() != 0);
        bool globalActivation = (globalActivationFunctions.size() != 0);
        for (size_t i = 0; i < numElements; ++i)
        {
            NNSetup::Topology& t = nn.topology.at(i);
            size_t const nsf = elements.at(i).numSymmetryFunctions();
            // Set input layer. Number of input layer neurons depends on NNP
            // type and NN purpose.
            if (nnpType == NNPType::HDNNP_2G)
            {
                // Can assume NN id is "short".
                t.numNeuronsPerLayer.at(0) = nsf;
            }
            else if (nnpType == NNPType::HDNNP_4G ||
                     nnpType == NNPType::HDNNP_Q)
            {
                // NN with id "elec" requires only SFs.
                if (k == "elec") t.numNeuronsPerLayer.at(0) = nsf;
                // "short" NN needs extra charge neuron.
                else if (k == "short") t.numNeuronsPerLayer.at(0) = nsf + 1;
            }
            // Set dummy input neuron activation function.
            t.activationFunctionsPerLayer.at(0) = NeuralNetwork::AF_IDENTITY;
            // Set output layer. Assume single output neuron.
            t.numNeuronsPerLayer.at(t.numLayers - 1) = 1;
            // If this element's NN does not use the global number of hidden
            // layers it makes no sense to set the global number of hidden
            // neurons or activation functions. Hence, skip the settings here,
            // appropriate settings should follow later in the per-element
            // section.
            if ((size_t)t.numLayers != globalNumHiddenLayers + 2) continue;
            for (int j = 1; j < t.numLayers; ++j)
            {
                if ((j == t.numLayers - 1) && globalActivation)
                {
                    t.activationFunctionsPerLayer.at(j) = activationFromString(
                        globalActivationFunctions.at(t.numLayers - 2));
                }
                else
                {
                    if (globalNumNeurons)
                    {
                        t.numNeuronsPerLayer.at(j) = atoi(
                            globalNumNeuronsPerHiddenLayer.at(j - 1).c_str());
                    }
                    if (globalActivation)
                    {
                        t.activationFunctionsPerLayer.at(j) =
                            activationFromString(
                                globalActivationFunctions.at(j - 1));
                    }
                }
            }
        }
        // Override global number of neurons with per-element keyword.
        keyword = "element_nodes" + nn.keywordSuffix;
        if (settings.keywordExists(keyword))
        {
            settings::Settings::KeyRange r = settings.getValues(keyword);
            for (settings::Settings::KeyMap::const_iterator it = r.first;
                 it != r.second; ++it)
            {
                vector<string> args = split(reduce(it->second.first));
                size_t e = elementMap[args.at(0)];
                size_t n = args.size() - 1;
                NNSetup::Topology& t = nn.topology.at(e);
                if ((size_t)t.numLayers != n + 2)
                {
                    throw runtime_error(strpr("ERROR: Inconsistent per-element"
                                              " NN topology keyword \"%s\".\n",
                                              keyword.c_str()));
                }
                for (int j = 1; j < t.numLayers - 2; ++j)
                {
                    t.numNeuronsPerLayer.at(j) = atoi(args.at(j).c_str());
                }
            }
        }
        // Override global activation functions with per-element keyword.
        keyword = "element_activation" + nn.keywordSuffix;
        if (settings.keywordExists(keyword))
        {
            settings::Settings::KeyRange r = settings.getValues(keyword);
            for (settings::Settings::KeyMap::const_iterator it = r.first;
                 it != r.second; ++it)
            {
                vector<string> args = split(reduce(it->second.first));
                size_t e = elementMap[args.at(0)];
                size_t n = args.size() - 1;
                NNSetup::Topology& t = nn.topology.at(e);
                if ((size_t)t.numLayers != n + 1)
                {
                    throw runtime_error(strpr("ERROR: Inconsistent per-element"
                                              " NN topology keyword \"%s\".\n",
                                              keyword.c_str()));
                }
                for (int j = 1; j < t.numLayers - 1; ++j)
                {
                    t.activationFunctionsPerLayer.at(j) =
                        activationFromString(args.at(j).c_str());
                }
            }
        }
 
        // Finally check everything for any unset NN property.
        for (size_t i = 0; i < numElements; ++i)
        {
            NNSetup::Topology const& t = nn.topology.at(i);
            for (int j = 0; j < t.numLayers; ++j)
            {
                if (t.numNeuronsPerLayer.at(j) == 0)
                {
                    throw runtime_error(strpr(
                              "ERROR: NN \"%s\", element %2s: number of "
                              "neurons for layer %d unset.\n",
                              nn.id.c_str(),
                              elements.at(i).getSymbol().c_str(),
                              j));
                }
                if (t.activationFunctionsPerLayer.at(j)
                        == NeuralNetwork::AF_UNSET)
                {
                    throw runtime_error(strpr(
                              "ERROR: NN \"%s\", element %2s: activation "
                              "functions for layer %d unset.\n",
                              nn.id.c_str(),
                              elements.at(i).getSymbol().c_str(),
                              j));
                }
            }
        }
    }
 
 
    bool normalizeNeurons = settings.keywordExists("normalize_nodes");
    log << strpr("Normalize neurons (all elements): %d\n",
                 (int)normalizeNeurons);
    log << "-----------------------------------------"
           "--------------------------------------\n";
 
    // Finally, allocate all neural networks.
    for (auto& k : nnk)
    {
        for (size_t i = 0; i < numElements; ++i)
        {
            Element& e = elements.at(i);
            NNSetup::Topology const& t = nns.at(k).topology.at(i);
            e.neuralNetworks.emplace(
                                    piecewise_construct,
                                    forward_as_tuple(k),
                                    forward_as_tuple(
                                        t.numLayers,
                                        t.numNeuronsPerLayer.data(),
                                        t.activationFunctionsPerLayer.data()));
            e.neuralNetworks.at(k).setNormalizeNeurons(normalizeNeurons);
            log << strpr("Atomic %s NN for "
                         "element %2s :\n",
                         nns.at(k).name.c_str(),
                         e.getSymbol().c_str());
            log << e.neuralNetworks.at(k).info();
            log << "-----------------------------------------"
                   "--------------------------------------\n";
        }
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::setupNeuralNetworkWeights(map<string, string> fileNameFormats)
{
    setupNeuralNetworkWeights("", fileNameFormats);
    return;
}
 
void Mode::setupNeuralNetworkWeights(string              directoryPrefix,
                                     map<string, string> fileNameFormats)
{
    log << "\n";
    log << "*** SETUP: NEURAL NETWORK WEIGHTS *******"
           "**************************************\n";
    log << "\n";
 
    for (auto k : nnk)
    {
        string actualFileNameFormat;
        if (fileNameFormats.find(k) != fileNameFormats.end())
        {
            actualFileNameFormat = fileNameFormats.at(k);
        }
        else actualFileNameFormat = nns.at(k).weightFileFormat;
        actualFileNameFormat = directoryPrefix + actualFileNameFormat;
        log << strpr("%s weight file name format: %s\n",
                     cap(nns.at(k).name).c_str(),
                     actualFileNameFormat.c_str());
        readNeuralNetworkWeights(k, actualFileNameFormat);
    }
 
    log << "*****************************************"
           "**************************************\n";
 
    return;
}
 
void Mode::calculateSymmetryFunctions(Structure& structure,
                                      bool const derivatives)
{
    // Skip calculation for whole structure if results are already saved.
    if (structure.hasSymmetryFunctionDerivatives) return;
    if (structure.hasSymmetryFunctions && !derivatives) return;
 
    Atom* a = NULL;
    Element* e = NULL;
#ifdef _OPENMP
    #pragma omp parallel for private (a, e)
#endif
    for (size_t i = 0; i < structure.atoms.size(); ++i)
    {
        // Pointer to atom.
        a = &(structure.atoms.at(i));
 
        // Skip calculation for individual atom if results are already saved.
        if (a->hasSymmetryFunctionDerivatives) continue;
        if (a->hasSymmetryFunctions && !derivatives) continue;
 
        // Inform atom if extra charge neuron is present in short-range NN.
        if (nnpType == NNPType::HDNNP_4G ||
            nnpType == NNPType::HDNNP_Q) a->useChargeNeuron = true;
 
        // Get element of atom and set number of symmetry functions.
        e = &(elements.at(a->element));
        a->numSymmetryFunctions = e->numSymmetryFunctions();
        if (derivatives)
        {
            a->numSymmetryFunctionDerivatives
                = e->getSymmetryFunctionNumTable();
        }
#ifndef N2P2_NO_SF_CACHE
        a->cacheSizePerElement = e->getCacheSizes();
#endif
 
#ifndef N2P2_NO_NEIGH_CHECK
        // Check if atom has low number of neighbors.
        size_t numNeighbors = a->calculateNumNeighbors(
                minCutoffRadius.at(e->getIndex()));
        if (numNeighbors < minNeighbors.at(e->getIndex()))
        {
            log << strpr("WARNING: Structure %6zu Atom %6zu : %zu "
                         "neighbors.\n",
                         a->indexStructure,
                         a->index,
                         numNeighbors);
        }
#endif
 
        // Allocate symmetry function data vectors in atom.
        a->allocate(derivatives, maxCutoffRadius);
 
        // Calculate symmetry functions (and derivatives).
        e->calculateSymmetryFunctions(*a, derivatives);
 
        // Remember that symmetry functions of this atom have been calculated.
        a->hasSymmetryFunctions = true;
        if (derivatives) a->hasSymmetryFunctionDerivatives = true;
    }
 
    // If requested, check extrapolation warnings or update statistics.
    // Needed to shift this out of the loop above to make it thread-safe.
    if (checkExtrapolationWarnings)
    {
        for (size_t i = 0; i < structure.atoms.size(); ++i)
        {
            a = &(structure.atoms.at(i));
            e = &(elements.at(a->element));
            e->updateSymmetryFunctionStatistics(*a);
        }
    }
 
    // Remember that symmetry functions of this structure have been calculated.
    structure.hasSymmetryFunctions = true;
    if (derivatives) structure.hasSymmetryFunctionDerivatives = true;
 
    return;
}
 
void Mode::calculateSymmetryFunctionGroups(Structure& structure,
                                           bool const derivatives)
{
    // Skip calculation for whole structure if results are already saved.
    if (structure.hasSymmetryFunctionDerivatives) return;
    if (structure.hasSymmetryFunctions && !derivatives) return;
 
    Atom* a = NULL;
    Element* e = NULL;
#ifdef _OPENMP
    #pragma omp parallel for private (a, e)
#endif
    for (size_t i = 0; i < structure.atoms.size(); ++i)
    {
        // Pointer to atom.
        a = &(structure.atoms.at(i));
 
        // Skip calculation for individual atom if results are already saved.
        if (a->hasSymmetryFunctionDerivatives) continue;
        if (a->hasSymmetryFunctions && !derivatives) continue;
 
        // Inform atom if extra charge neuron is present in short-range NN.
        if (nnpType == NNPType::HDNNP_4G ||
            nnpType == NNPType::HDNNP_Q) a->useChargeNeuron = true;
 
        // Get element of atom and set number of symmetry functions.
        e = &(elements.at(a->element));
        a->numSymmetryFunctions = e->numSymmetryFunctions();
        if (derivatives)
        {
            a->numSymmetryFunctionDerivatives
                = e->getSymmetryFunctionNumTable();
        }
#ifndef N2P2_NO_SF_CACHE
        a->cacheSizePerElement = e->getCacheSizes();
#endif
 
#ifndef N2P2_NO_NEIGH_CHECK
        // Check if atom has low number of neighbors.
        size_t numNeighbors = a->calculateNumNeighbors(
                minCutoffRadius.at(e->getIndex()));
        if (numNeighbors < minNeighbors.at(e->getIndex()))
        {
            log << strpr("WARNING: Structure %6zu Atom %6zu : %zu "
                         "neighbors.\n",
                         a->indexStructure,
                         a->index,
                         numNeighbors);
        }
#endif
 
        // Allocate symmetry function data vectors in atom.
        a->allocate(derivatives, maxCutoffRadius);
 
        // Calculate symmetry functions (and derivatives).
        e->calculateSymmetryFunctionGroups(*a, derivatives);
 
        // Remember that symmetry functions of this atom have been calculated.
        a->hasSymmetryFunctions = true;
        if (derivatives) a->hasSymmetryFunctionDerivatives = true;
    }
 
    // If requested, check extrapolation warnings or update statistics.
    // Needed to shift this out of the loop above to make it thread-safe.
    if (checkExtrapolationWarnings)
    {
        for (size_t i = 0; i < structure.atoms.size(); ++i)
        {
            a = &(structure.atoms.at(i));
            e = &(elements.at(a->element));
            e->updateSymmetryFunctionStatistics(*a);
        }
    }
 
    // Remember that symmetry functions of this structure have been calculated.
    structure.hasSymmetryFunctions = true;
    if (derivatives) structure.hasSymmetryFunctionDerivatives = true;
 
    return;
}
 
void Mode::calculateAtomicNeuralNetworks(Structure& structure,
                                         bool const derivatives,
                                         string id)
{
    if (id == "") id = nnk.front();
 
    if (nnpType == NNPType::HDNNP_2G)
    {
        for (vector<Atom>::iterator it = structure.atoms.begin();
             it != structure.atoms.end(); ++it)
        {
            NeuralNetwork& nn = elements.at(it->element)
                                .neuralNetworks.at(id);
            nn.setInput(&((it->G).front()));
            nn.propagate();
            if (derivatives)
            {
                nn.calculateDEdG(&((it->dEdG).front()));
            }
            nn.getOutput(&(it->energy));
        }
    }
    else if (nnpType == NNPType::HDNNP_4G)
    {
        if (id == "elec")
        {
            for (auto& a : structure.atoms)
            {
                NeuralNetwork& nn = elements.at(a.element)
                                    .neuralNetworks.at(id);
                nn.setInput(&((a.G).front()));
                nn.propagate();
                if (derivatives)
                {
                    // Calculation of dEdG is identical to dChidG
                    nn.calculateDEdG(&((a.dChidG).front()));
                    if (normalize)
                    {
                        for (auto& dChidGi : a.dChidG)
                            dChidGi = normalized("negativity", dChidGi);
                    }
                }
                nn.getOutput(&(a.chi));
                //log << strpr("Atom %5zu (%2s) chi: %24.16E\n",
                //             a.index, elementMap[a.element].c_str(), a.chi);
                if (normalize) a.chi = normalized("negativity", a.chi);
            }
        }
        else if (id == "short")
        {
            for (auto& a : structure.atoms)
            {
                NeuralNetwork& nn = elements.at(a.element)
                                    .neuralNetworks.at(id);
                // TODO: This part should simplify with improved NN class.
                for (size_t i = 0; i < a.G.size(); ++i)
                {
                    nn.setInput(i, a.G.at(i));
                }
                // Set additional charge neuron.
                nn.setInput(a.G.size(), a.charge);
                nn.propagate();
                if (derivatives)
                {
                    // last element of vector dEdG is dEdQ
                    nn.calculateDEdG(&((a.dEdG).front()));
                }
                nn.getOutput(&(a.energy));
                //log << strpr("Atom %5zu (%2s) energy: %24.16E\n",
                //             a.index, elementMap[a.element].c_str(), a.energy);
            }
        }
    }
    else if (nnpType == NNPType::HDNNP_Q)
    {
        // Ignore ID, both NNs are computed here.
        for (vector<Atom>::iterator it = structure.atoms.begin();
             it != structure.atoms.end(); ++it)
        {
            // First the charge NN.
            NeuralNetwork& nnCharge = elements.at(it->element)
                                      .neuralNetworks.at("elec");
            nnCharge.setInput(&((it->G).front()));
            nnCharge.propagate();
            if (derivatives)
            {
                nnCharge.calculateDEdG(&((it->dQdG).front()));
            }
            nnCharge.getOutput(&(it->charge));
 
            // Now the short-range NN (have to set input neurons individually).
            NeuralNetwork& nnShort = elements.at(it->element)
                                     .neuralNetworks.at("short");
            // TODO: This part should simplify with improved NN class.
            for (size_t i = 0; i < it->G.size(); ++i)
            {
                nnShort.setInput(i, it->G.at(i));
            }
            // Set additional charge neuron.
            nnShort.setInput(it->G.size(), it->charge);
            nnShort.propagate();
            if (derivatives)
            {
                nnShort.calculateDEdG(&((it->dEdG).front()));
            }
            nnShort.getOutput(&(it->energy));
        }
    }
 
    return;
}
 
// TODO: Make this const?
void Mode::chargeEquilibration( Structure& structure,
                                bool const derivativesElec)
{
    Structure& s = structure;
 
    // Prepare hardness vector and precalculate gamma(i, j).
    VectorXd hardness(numElements);
    MatrixXd gammaSqrt2(numElements, numElements);
    VectorXd sigmaSqrtPi(numElements);
    // In case of natural units and no normalization
    double fourPiEps = 1;
    // In case of natural units but with normalization. Other units currently
    // not supported.
    if (normalize) fourPiEps = pow(convCharge, 2) / (convLength * convEnergy);
 
    fourPiEps = 1;
 
    for (size_t i = 0; i < numElements; ++i)
    {
        hardness(i) = elements.at(i).getHardness();
        double const iSigma = elements.at(i).getQsigma();
        sigmaSqrtPi(i) = sqrt(M_PI) * iSigma;
        for (size_t j = 0; j < numElements; ++j)
        {
            double const jSigma = elements.at(j).getQsigma();
            gammaSqrt2(i, j) = sqrt(2.0 * (iSigma * iSigma + jSigma * jSigma));
        }
    }
 
    s.calculateElectrostaticEnergy(ewaldSetup,
                                   hardness,
                                   gammaSqrt2,
                                   sigmaSqrtPi,
                                   screeningFunction,
                                   fourPiEps,
                                   erfcBuf);
 
    // TODO: leave these 2 functions here or move it to e.g. forces? Needs to be
    // executed after calculateElectrostaticEnergy.
    if (derivativesElec)
    {
        s.calculateDAdrQ(ewaldSetup, gammaSqrt2, fourPiEps, erfcBuf);
        s.calculateElectrostaticEnergyDerivatives(hardness,
                                                  gammaSqrt2,
                                                  sigmaSqrtPi,
                                                  screeningFunction,
                                                  fourPiEps);
    }
 
    return;
}
 
void Mode::calculateEnergy(Structure& structure) const
{
    // Loop over all atoms and add atomic contributions to total energy.
    structure.energy = 0.0;
    structure.energyShort = 0.0;
    for (vector<Atom>::iterator it = structure.atoms.begin();
         it != structure.atoms.end(); ++it)
    {
        structure.energyShort += it->energy;
    }
    structure.energy = structure.energyShort + structure.energyElec;
 
    //cout << strpr("Electrostatic energy: %24.16E\n", structure.energyElec);
    //cout << strpr("Short-range   energy: %24.16E\n", structure.energyShort);
    //cout << strpr("Sum           energy: %24.16E\n", structure.energy);
    //cout << strpr("Offset        energy: %24.16E\n", getEnergyOffset(structure));
    //cout << "---------------------\n";
    //cout << strpr("Total         energy: %24.16E\n", structure.energy + getEnergyOffset(structure));
    //cout << strpr("Reference     energy: %24.16E\n", structure.energyRef + getEnergyOffset(structure));
    //cout << "---------------------\n";
    //cout << "without offset:      \n";
    //cout << strpr("Total         energy: %24.16E\n", structure.energy);
    //cout << strpr("Reference     energy: %24.16E\n", structure.energyRef);
 
    return;
}
 
void Mode::calculateCharge(Structure& structure) const
{
    // Loop over all atoms and add atomic charge contributions to total charge.
    structure.charge = 0.0;
    for (vector<Atom>::iterator it = structure.atoms.begin();
         it != structure.atoms.end(); ++it)
    {
        //log << strpr("Atom %5zu (%2s) q: %24.16E\n",
        //             it->index, elementMap[it->element].c_str(), it->charge);
        structure.charge += it->charge;
    }
 
    //log << "---------------------\n";
    //log << strpr("Total         charge: %24.16E\n", structure.charge);
    //log << strpr("Reference     charge: %24.16E\n", structure.chargeRef);
 
    return;
}
 
void Mode::calculateForces(Structure& structure) const
{
    if (nnpType == NNPType::HDNNP_Q)
    {
        throw runtime_error("WARNING: Forces are not implemented yet.\n");
        return;
    }
 
    // Loop over all atoms, center atom i (ai).
#ifdef _OPENMP
    #pragma omp parallel
    {
    #pragma omp for
#endif
    for (size_t i = 0; i < structure.atoms.size(); ++i) {
        // Set pointer to atom.
        Atom &ai = structure.atoms.at(i);
 
        // Reset forces.
        ai.f = Vec3D{};
 
        // First add force contributions from atom i itself (gradient of
        // atomic energy E_i).
        ai.f += ai.calculateSelfForceShort();
 
        // Now loop over all neighbor atoms j of atom i. These may hold
        // non-zero derivatives of their symmetry functions with respect to
        // atom i's coordinates. Some atoms may appear multiple times in the
        // neighbor list because of periodic boundary conditions. To avoid
        // that the same contributions are added multiple times use the
        // "unique neighbor" list. This list contains also the central atom
        // index as first entry and hence also adds contributions of periodic
        // images of the central atom (happens when cutoff radii larger than
        // cell vector lengths are used, but this is already considered in the
        // self-interaction).
        for (vector<size_t>::const_iterator it = ai.neighborsUnique.begin() + 1;
             it != ai.neighborsUnique.end(); ++it)
        {
            // Define shortcut for atom j (aj).
            Atom &aj = structure.atoms.at(*it);
#ifndef N2P2_FULL_SFD_MEMORY
            vector<vector<size_t>> const& tableFull
                = elements.at(aj.element).getSymmetryFunctionTable();
#endif
            // Loop over atom j's neighbors (n), atom i should be one of them.
            // TODO: Could implement maxCutoffRadiusSymFunc for each element and
            //       use this instead of maxCutoffRadius.
            size_t const numNeighbors =
                aj.getStoredMinNumNeighbors(maxCutoffRadius);
            for (size_t k = 0; k < numNeighbors; ++k) {
                Atom::Neighbor const &n = aj.neighbors[k];
                // If atom j's neighbor is atom i add force contributions.
                if (n.index == ai.index)
                {
#ifndef N2P2_FULL_SFD_MEMORY
                    ai.f += aj.calculatePairForceShort(n, &tableFull);
#else
                    ai.f += aj.calculatePairForceShort(n);
#endif
                }
            }
        }
    }
 
    if (nnpType == NNPType::HDNNP_4G)
    {
        Structure &s = structure;
 
        VectorXd lambdaTotal = s.calculateForceLambdaTotal();
        VectorXd lambdaElec = s.calculateForceLambdaElec();
 
#ifdef _OPENMP
        #pragma omp for
#endif
        // OpenMP 4.0 doesn't support range based loops
        for (size_t i = 0; i < s.numAtoms; ++i)
        {
            auto &ai = s.atoms[i];
            ai.f -= ai.pEelecpr;
            ai.fElec = -ai.pEelecpr;
 
            for (size_t j = 0; j < s.numAtoms; ++j)
            {
                Atom const &aj = s.atoms.at(j);
 
#ifndef N2P2_FULL_SFD_MEMORY
                vector<vector<size_t> > const &tableFull
                        = elements.at(aj.element).getSymmetryFunctionTable();
                Vec3D dChidr = aj.calculateDChidr(ai.index,
                                                  maxCutoffRadius,
                                                  &tableFull);
#else
                Vec3D dChidr = aj.calculateDChidr(ai.index,
                                                  maxCutoffRadius);
#endif
                ai.f -= lambdaTotal(j) * (ai.dAdrQ[j] + dChidr);
                ai.fElec -= lambdaElec(j) * (ai.dAdrQ[j] + dChidr);
            }
        }
    }
#ifdef _OPENMP
    }
#endif
    return;
}
 
 
void Mode::evaluateNNP(Structure& structure, bool useForces, bool useDEdG)
{
    useDEdG = (useForces || useDEdG);
    if (nnpType == NNPType::HDNNP_4G)
    {
        structure.calculateMaxCutoffRadiusOverall(
            ewaldSetup,
            screeningFunction.getOuter(),
            maxCutoffRadius);
        structure.calculateNeighborList(maxCutoffRadius, cutoffs);
    }
    // TODO: For the moment sort neighbors only for 4G-HDNNPs (breaks some
    //       CI tests because of small numeric changes).
    else structure.calculateNeighborList(maxCutoffRadius, false);
 
#ifdef N2P2_NO_SF_GROUPS
    calculateSymmetryFunctions(structure, true);
#else
    calculateSymmetryFunctionGroups(structure, useForces);
#endif
 
    // Needed if useForces == false but sensitivity data is requested.
    if (useDEdG && !useForces)
    {
        // Manually allocate dEdG vectors.
        for (auto& a : structure.atoms)
        {
            if (nnpType == NNPType::HDNNP_4G)
            {
                a.dEdG.resize(a.numSymmetryFunctions + 1, 0.0);
                a.dChidG.resize(a.numSymmetryFunctions, 0.0);
            }
            else
                a.dEdG.resize(a.numSymmetryFunctions, 0.0);
        }
    }
 
    calculateAtomicNeuralNetworks(structure, useDEdG);
    if (nnpType == NNPType::HDNNP_4G)
    {
        chargeEquilibration(structure, useForces);
        calculateAtomicNeuralNetworks(structure, useDEdG, "short");
    }
    calculateEnergy(structure);
    if (nnpType == NNPType::HDNNP_4G ||
        nnpType == NNPType::HDNNP_Q)
        calculateCharge(structure);
    if (useForces) calculateForces(structure);
}
 
void Mode::addEnergyOffset(Structure& structure, bool ref)
{
    for (size_t i = 0; i < numElements; ++i)
    {
        if (ref)
        {
            structure.energyRef += structure.numAtomsPerElement.at(i)
                                   * elements.at(i).getAtomicEnergyOffset();
        }
        else
        {
            structure.energy += structure.numAtomsPerElement.at(i)
                              * elements.at(i).getAtomicEnergyOffset();
        }
    }
 
    return;
}
 
void Mode::removeEnergyOffset(Structure& structure, bool ref)
{
    for (size_t i = 0; i < numElements; ++i)
    {
        if (ref)
        {
            structure.energyRef -= structure.numAtomsPerElement.at(i)
                                 * elements.at(i).getAtomicEnergyOffset();
        }
        else
        {
            structure.energy -= structure.numAtomsPerElement.at(i)
                              * elements.at(i).getAtomicEnergyOffset();
        }
    }
 
    return;
}
 
double Mode::getEnergyOffset(Structure const& structure) const
{
    double result = 0.0;
 
    for (size_t i = 0; i < numElements; ++i)
    {
        result += structure.numAtomsPerElement.at(i)
                * elements.at(i).getAtomicEnergyOffset();
    }
 
    return result;
}
 
double Mode::getEnergyWithOffset(Structure const& structure, bool ref) const
{
    double result;
    if (ref) result = structure.energyRef;
    else     result = structure.energy;
 
    for (size_t i = 0; i < numElements; ++i)
    {
        result += structure.numAtomsPerElement.at(i)
                * elements.at(i).getAtomicEnergyOffset();
    }
 
    return result;
}
 
double Mode::normalized(string const& property, double value) const
{
    if      (property == "energy") return value * convEnergy;
    else if (property == "force") return value * convEnergy / convLength;
    else if (property == "charge") return value * convCharge;
    else if (property == "hardness")
        return value * convEnergy / pow(convCharge, 2);
    else if (property == "negativity") return value * convEnergy / convCharge;
    else throw runtime_error("ERROR: Unknown property to convert to "
                             "normalized units.\n");
}
 
double Mode::normalizedEnergy(Structure const& structure, bool ref) const
{
    if (ref)
    {
        return (structure.energyRef - structure.numAtoms * meanEnergy)
               * convEnergy;
    }
    else
    {
        return (structure.energy - structure.numAtoms * meanEnergy)
               * convEnergy;
    }
}
 
double Mode::physical(string const& property, double value) const
{
    if      (property == "energy") return value / convEnergy;
    else if (property == "force") return value * convLength / convEnergy;
    else if (property == "charge") return value / convCharge;
    else if (property == "hardness")
        return value / (convEnergy / pow(convCharge, 2));
    else if (property == "negativity") return value * convCharge / convEnergy;
    else throw runtime_error("ERROR: Unknown property to convert to physical "
                             "units.\n");
}
 
double Mode::physicalEnergy(Structure const& structure, bool ref) const
{
    if (ref)
    {
        return structure.energyRef / convEnergy + structure.numAtoms
               * meanEnergy;
    }
    else
    {
        return structure.energy / convEnergy + structure.numAtoms * meanEnergy;
    }
}
 
void Mode::convertToNormalizedUnits(Structure& structure) const
{
    structure.toNormalizedUnits(meanEnergy, convEnergy, convLength, convCharge);
 
    return;
}
 
void Mode::convertToPhysicalUnits(Structure& structure) const
{
    structure.toPhysicalUnits(meanEnergy, convEnergy, convLength, convCharge);
 
    return;
}
 
void Mode::logEwaldCutoffs()
{
    if (nnpType != NNPType::HDNNP_4G) return;
    ewaldSetup.logEwaldCutoffs(log, convLength);
}
 
void Mode::resetExtrapolationWarnings()
{
    for (vector<Element>::iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        it->statistics.resetExtrapolationWarnings();
    }
 
    return;
}
 
size_t Mode::getNumExtrapolationWarnings() const
{
    size_t numExtrapolationWarnings = 0;
 
    for (vector<Element>::const_iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        numExtrapolationWarnings +=
            it->statistics.countExtrapolationWarnings();
    }
 
    return numExtrapolationWarnings;
}
 
vector<size_t> Mode::getNumSymmetryFunctions() const
{
    vector<size_t> v;
 
    for (vector<Element>::const_iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        v.push_back(it->numSymmetryFunctions());
    }
 
    return v;
}
 
bool Mode::settingsKeywordExists(std::string const& keyword) const
{
    return settings.keywordExists(keyword);
}
 
string Mode::settingsGetValue(std::string const& keyword) const
{
    return settings.getValue(keyword);
}
 
 
void Mode::writePrunedSettingsFile(vector<size_t> prune, string fileName) const
{
    ofstream file(fileName.c_str());
    vector<string> settingsLines = settings.getSettingsLines();
    for (size_t i = 0; i < settingsLines.size(); ++i)
    {
        if (find(prune.begin(), prune.end(), i) != prune.end())
        {
            file << "# ";
        }
        file << settingsLines.at(i) << '\n';
    }
    file.close();
 
    return;
}
 
void Mode::writeSettingsFile(ofstream* const& file) const
{
    settings.writeSettingsFile(file);
 
    return;
}
 
vector<size_t> Mode::pruneSymmetryFunctionsRange(double threshold)
{
    vector<size_t> prune;
 
    // Check if symmetry functions have low range.
    for (vector<Element>::const_iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        for (size_t i = 0; i < it->numSymmetryFunctions(); ++i)
        {
            SymFnc const& s = it->getSymmetryFunction(i);
            if (fabs(s.getGmax() - s.getGmin()) < threshold)
            {
                prune.push_back(it->getSymmetryFunction(i).getLineNumber());
            }
        }
    }
 
    return prune;
}
 
vector<size_t> Mode::pruneSymmetryFunctionsSensitivity(
                                           double                  threshold,
                                           vector<vector<double> > sensitivity)
{
    vector<size_t> prune;
 
    for (size_t i = 0; i < numElements; ++i)
    {
        for (size_t j = 0; j < elements.at(i).numSymmetryFunctions(); ++j)
        {
            if (sensitivity.at(i).at(j) < threshold)
            {
                prune.push_back(
                        elements.at(i).getSymmetryFunction(j).getLineNumber());
            }
        }
    }
 
    return prune;
}
 
void Mode::readNeuralNetworkWeights(string const& id,
                                    string const& fileNameFormat)
{
    for (vector<Element>::iterator it = elements.begin();
         it != elements.end(); ++it)
    {
        string fileName = strpr(fileNameFormat.c_str(),
                                it->getAtomicNumber());
        log << strpr("Setting weights for element %2s from file: %s\n",
                     it->getSymbol().c_str(),
                     fileName.c_str());
        vector<double> weights = readColumnsFromFile(fileName,
                                                     vector<size_t>(1, 0)
                                                    ).at(0);
        NeuralNetwork& nn = it->neuralNetworks.at(id);
        nn.setConnections(&(weights.front()));
    }
 
    return;
}