rpa.cc

Go to the documentation of this file.

/*
 *            Copyright 2009-2021 The VOTCA Development Team
 *                       (http://www.votca.org)
 *
 *      Licensed under the Apache License, Version 2.0 (the "License")
 *
 * You may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *              http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 */
 
// Local VOTCA includes
#include "votca/xtp/rpa.h"
#include "votca/xtp/aomatrix.h"
#include "votca/xtp/openmp_cuda.h"
#include "votca/xtp/threecenter.h"
#include "votca/xtp/vc2index.h"
#include <iomanip>
#include <sstream>
 
namespace votca {
namespace xtp {
 
void RPA::UpdateRPAInputEnergies(const Eigen::VectorXd& dftenergies,
                                 const Eigen::VectorXd& gwaenergies,
                                 Index qpmin) {
  Index rpatotal = rpamax_ - rpamin_ + 1;
  energies_ = dftenergies.segment(rpamin_, rpatotal);
  Index gwsize = Index(gwaenergies.size());
 
  energies_.segment(qpmin - rpamin_, gwsize) = gwaenergies;
 
  ShiftUncorrectedEnergies(dftenergies, qpmin, gwsize);
}
 
// Shifts energies of levels that are not QP corrected but
// used in the RPA:
// between rpamin and qpmin: by maximum abs of explicit QP corrections
//                           from qpmin to HOMO
// between qpmax and rpamax: by maximum abs of explicit QP corrections
//                           from LUMO to qpmax
void RPA::ShiftUncorrectedEnergies(const Eigen::VectorXd& dftenergies,
                                   Index qpmin, Index gwsize) {
 
  Index lumo = homo_ + 1;
  Index qpmax = qpmin + gwsize - 1;
 
  // get max abs QP corrections for occupied/virtual levels
  double max_correction_occ = getMaxCorrection(dftenergies, qpmin, homo_);
  double max_correction_virt = getMaxCorrection(dftenergies, lumo, qpmax);
 
  // shift energies
  energies_.head(qpmin).array() -= max_correction_occ;
  energies_.tail(rpamax_ - qpmax).array() += max_correction_virt;
}
 
double RPA::getMaxCorrection(const Eigen::VectorXd& dftenergies, Index min,
                             Index max) const {
 
  Index range = max - min + 1;
  Eigen::VectorXd corrections =
      energies_.segment(min, range) - dftenergies.segment(min - rpamin_, range);
 
  return (corrections.cwiseAbs()).maxCoeff();
}
 
template <bool imag>
Eigen::MatrixXd RPA::calculate_epsilon(double frequency) const {
  const Index size = Mmn_.auxsize();
 
  const Index lumo = homo_ + 1;
  const Index n_occ = lumo - rpamin_;
  const Index n_unocc = rpamax_ - lumo + 1;
  const double freq2 = frequency * frequency;
  const double eta2 = eta_ * eta_;
 
  OpenMP_CUDA transform;
  transform.createTemporaries(n_unocc, size);
 
#pragma omp parallel
  {
    Index threadid = OPENMP::getThreadId();
#pragma omp for schedule(dynamic)
    for (Index m_level = 0; m_level < n_occ; m_level++) {
      const double qp_energy_m = energies_(m_level);
 
      Eigen::MatrixXd Mmn_RPA = Mmn_[m_level].bottomRows(n_unocc);
      transform.PushMatrix(Mmn_RPA, threadid);
      const Eigen::ArrayXd deltaE =
          energies_.tail(n_unocc).array() - qp_energy_m;
      Eigen::VectorXd denom;
      if (imag) {
        denom = 4 * deltaE / (deltaE.square() + freq2);
      } else {
        Eigen::ArrayXd deltEf = deltaE - frequency;
        Eigen::ArrayXd sum = deltEf / (deltEf.square() + eta2);
        deltEf = deltaE + frequency;
        sum += deltEf / (deltEf.square() + eta2);
        denom = 2 * sum;
      }
 
      transform.A_TDA(denom, threadid);
    }
  }
  Eigen::MatrixXd result = transform.getReductionVar();
  result.diagonal().array() += 1.0;
  return result;
}
 
template Eigen::MatrixXd RPA::calculate_epsilon<true>(double frequency) const;
template Eigen::MatrixXd RPA::calculate_epsilon<false>(double frequency) const;
 
Eigen::MatrixXd RPA::calculate_epsilon_r(std::complex<double> frequency) const {
 
  const Index size = Mmn_.auxsize();
 
  const Index lumo = homo_ + 1;
  const Index n_occ = lumo - rpamin_;
  const Index n_unocc = rpamax_ - lumo + 1;
  OpenMP_CUDA transform;
  transform.createTemporaries(n_unocc, size);
#pragma omp parallel
  {
    Index threadid = OPENMP::getThreadId();
#pragma omp for schedule(dynamic)
    for (Index m_level = 0; m_level < n_occ; m_level++) {
 
      const double qp_energy_m = energies_(m_level);
      Eigen::MatrixXd Mmn_RPA = Mmn_[m_level].bottomRows(n_unocc);
      transform.PushMatrix(Mmn_RPA, threadid);
      const Eigen::ArrayXd deltaE =
          energies_.tail(n_unocc).array() - qp_energy_m;
 
      Eigen::ArrayXd deltaEm = frequency.real() - deltaE;
      Eigen::ArrayXd deltaEp = frequency.real() + deltaE;
 
      double sigma_1 = std::pow(frequency.imag() + eta_, 2);
      double sigma_2 = std::pow(frequency.imag() - eta_, 2);
 
      Eigen::VectorXd chi =
          deltaEm * (deltaEm.cwiseAbs2() + sigma_1).cwiseInverse() -
          deltaEp * (deltaEp.cwiseAbs2() + sigma_2).cwiseInverse();
      transform.A_TDA(chi, threadid);
    }
  }
  Eigen::MatrixXd result = -2 * transform.getReductionVar();
  result.diagonal().array() += 1.0;
  return result;
}
 
RPA::rpa_eigensolution RPA::Diagonalize_H2p() const {
  const Index lumo = homo_ + 1;
  const Index n_occ = lumo - rpamin_;
  const Index n_unocc = rpamax_ - lumo + 1;
  const Index rpasize = n_occ * n_unocc;
 
  Eigen::VectorXd AmB = Calculate_H2p_AmB();
  Eigen::MatrixXd ApB = Calculate_H2p_ApB();
 
  RPA::rpa_eigensolution sol;
  sol.ERPA_correlation = -0.25 * (ApB.trace() + AmB.sum());
 
  // C = AmB^1/2 * ApB * AmB^1/2
  Eigen::MatrixXd& C = ApB;
  C.applyOnTheLeft(AmB.cwiseSqrt().asDiagonal());
  C.applyOnTheRight(AmB.cwiseSqrt().asDiagonal());
 
  Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es = Diagonalize_H2p_C(C);
 
  // Do not remove this line! It has to be there for MKL to not crash
  sol.omega = Eigen::VectorXd::Zero(es.eigenvalues().size());
  sol.omega = es.eigenvalues().cwiseSqrt();
  sol.ERPA_correlation += 0.5 * sol.omega.sum();
 
  {
    std::ostringstream oss;
    oss << TimeStamp() << " RKS H2p poles (first 20, eV): ";
    const Index nprint = std::min<Index>(20, sol.omega.size());
    for (Index i = 0; i < nprint; ++i) {
      oss << std::fixed << std::setprecision(6)
          << tools::conv::hrt2ev * sol.omega(i);
      if (i + 1 < nprint) {
        oss << ", ";
      }
    }
    XTP_LOG(Log::error, log_) << oss.str() << std::flush;
  }
 
  XTP_LOG(Log::info, log_) << TimeStamp()
                           << " Lowest neutral excitation energy (eV): "
                           << tools::conv::hrt2ev * sol.omega.minCoeff()
                           << std::flush;
 
  // RPA correlation energy calculated from Eq.9 of J. Chem. Phys. 132, 234114
  // (2010)
  XTP_LOG(Log::error, log_)
      << TimeStamp()
      << " RPA correlation energy (Hartree): " << sol.ERPA_correlation
      << std::flush;
 
  sol.XpY = Eigen::MatrixXd(rpasize, rpasize);
 
  Eigen::VectorXd AmB_sqrt = AmB.cwiseSqrt();
  Eigen::VectorXd Omega_sqrt_inv = sol.omega.cwiseSqrt().cwiseInverse();
  for (int s = 0; s < rpasize; s++) {
    sol.XpY.col(s) =
        Omega_sqrt_inv(s) * AmB_sqrt.cwiseProduct(es.eigenvectors().col(s));
  }
 
  return sol;
}
 
Eigen::VectorXd RPA::Calculate_H2p_AmB() const {
  const Index lumo = homo_ + 1;
  const Index n_occ = lumo - rpamin_;
  const Index n_unocc = rpamax_ - lumo + 1;
  const Index rpasize = n_occ * n_unocc;
  vc2index vc = vc2index(0, 0, n_unocc);
  Eigen::VectorXd AmB = Eigen::VectorXd::Zero(rpasize);
  for (Index v = 0; v < n_occ; v++) {
    Index i = vc.I(v, 0);
    AmB.segment(i, n_unocc) =
        energies_.segment(n_occ, n_unocc).array() - energies_(v);
  }
  return AmB;
}
 
Eigen::MatrixXd RPA::Calculate_H2p_ApB() const {
  const Index lumo = homo_ + 1;
  const Index n_occ = lumo - rpamin_;
  const Index n_unocc = rpamax_ - lumo + 1;
  const Index rpasize = n_occ * n_unocc;
  vc2index vc = vc2index(0, 0, n_unocc);
  Eigen::MatrixXd ApB = Eigen::MatrixXd::Zero(rpasize, rpasize);
#pragma omp parallel for schedule(guided)
  for (Index v2 = 0; v2 < n_occ; v2++) {
    Index i2 = vc.I(v2, 0);
    const Eigen::MatrixXd Mmn_v2T =
        Mmn_[v2].middleRows(n_occ, n_unocc).transpose();
    for (Index v1 = v2; v1 < n_occ; v1++) {
      Index i1 = vc.I(v1, 0);
      // Multiply with factor 2 to sum over both (identical) spin states
      ApB.block(i1, i2, n_unocc, n_unocc) =
          2 * 2 * Mmn_[v1].middleRows(n_occ, n_unocc) * Mmn_v2T;
    }
  }
  ApB.diagonal() += Calculate_H2p_AmB();
  return ApB;
}
 
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> RPA::Diagonalize_H2p_C(
    const Eigen::MatrixXd& C) const {
  XTP_LOG(Log::error, log_)
      << TimeStamp() << " Diagonalizing two-particle Hamiltonian "
      << std::flush;
  Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es(C);  // Uses lower triangle
  XTP_LOG(Log::error, log_)
      << TimeStamp() << " Diagonalization done " << std::flush;
  double minCoeff = es.eigenvalues().minCoeff();
  if (minCoeff <= 0.0) {
    XTP_LOG(Log::error, log_)
        << TimeStamp() << " Detected non-positive eigenvalue: " << minCoeff
        << std::flush;
    throw std::runtime_error("Detected non-positive eigenvalue.");
  }
  return es;
}
 
}  // namespace xtp
}  // namespace votca