A Reduced Order Model approach for First-Principles Molecular Dynamics Computations

TL;DR

This paper introduces a data-driven reduced order model for first-principles molecular dynamics that constructs a low-dimensional basis to efficiently approximate electronic structures, enabling faster simulations without iterative wavefunction optimization.

Contribution

It presents a novel framework that bypasses explicit wavefunction optimization by using a reduced basis derived from representative configurations, improving computational efficiency in first-principles MD.

Findings

Accurately reproduces structural properties of water in MD simulations.

Reduces computational cost by avoiding iterative wavefunction optimization.

Demonstrates effectiveness in a Born-Oppenheimer molecular dynamics setting.

Abstract

To leverage the redundancy between the electronic structure computed at each step of first-principles molecular dynamics, we present a data-driven modeling framework for Kohn-Sham Density Functional Theory that bypasses the explicit optimization of electronic wavefunctions. We sample a priori representative atomic configurations and construct a low-dimensional basis that efficiently approximates the electronic structure subspace. Subsequently, we employ this reduced basis in a direct solver for the electronic single particle density matrix, thereby enabling the efficient determination of ground state without iterative wavefunction optimization. We demonstrate the efficacy of our approach in a Born-Oppenheimer molecular dynamics of a water molecule, showing that the resulting simulations accurately reproduce key structural properties, such as bond lengths and bond angle, obtained from full first-principles molecular dynamics. This work highlights the potential of data-driven approaches to develop efficient electronic structure solvers for first-principles simulations.