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Shadow Energy Functionals and Potentials in Born-Oppenheimer Molecular Dynamics (2302.06703v1)

Published 13 Feb 2023 in physics.chem-ph

Abstract: In Born-Oppenheimer molecular dynamics (BOMD) simulations based on density functional theory (DFT), the potential energy and the interatomic forces are calculated from an electronic ground state density that is determined by an iterative self-consistent field optimization procedure, which in practice never is fully converged. The calculated energies and the forces are therefore only approximate, which may lead to an unphysical energy drift and instabilities. Here we discuss an alternative shadow BOMD approach that is based on a backward error analysis. Instead of calculating approximate solutions for an underlying exact regular BO potential, we do the opposite. Instead, we calculate the exact electron density, energies, and forces, but for an underlying approximate shadow BO potential. In this way the calculated forces are conservative with respect to the shadow potential and generate accurate molecular trajectories with long-term energy stability. We show how such shadow BO potentials can be constructed at different levels of accuracy as a function of the integration time step, dt, from the minimization of a sequence of systematically improvable, but approximate, shadow energy density functionals. For each functional there is a corresponding ground state BO potential. These pairs of shadow energy functionals and potentials are higher-level generalizations of the original "0th-level" shadow energy functionals and potentials used in extended Lagrangian BOMD [Eur. Phys. J. B vol. 94, 164 (2021)]. The proposed shadow energy functionals and potentials are useful only within this dynamical framework, where also the electronic degrees of freedom are propagated together with the atomic positions and velocities. The theory is general and can be applied to MD simulations using approximate DFT, Hartree-Fock or semi-empirical methods, as well as to coarse-grained flexible charge models.

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