An adaptive mass algorithm for Car-Parrinello and Ehrenfest ab initio molecular dynamics

TL;DR

This paper introduces a dynamic algorithm for selecting the artificial electron mass in Car-Parrinello and Ehrenfest molecular dynamics, enhancing efficiency and accuracy without solving eigenvalue problems at each step.

Contribution

The paper proposes a novel adaptive mass algorithm based on Landau-Zener probability, improving the efficiency of ab initio molecular dynamics methods.

Findings

Adaptive mass improves computational efficiency

Method depends only on problem data

Numerical experiments validate effectiveness

Abstract

Ehrenfest and Car-Parrinello molecular dynamics are computational alternatives to approximate Born-Oppenheimer molecular dynamics without solving the electron eigenvalue problem at each time-step. A non-trivial issue is to choose the artificial electron mass parameter appearing in the Car-Parrinello method to achieve both good accuracy and high computational efficiency. In this paper, we propose an algorithm, motivated by the Landau-Zener probability, to systematically choose an artificial mass dynamically, which makes the Car-Parrinello and Ehrenfest molecular dynamics methods dependent only on the problem data. Numerical experiments for simple model problems show that the time-dependent adaptive artificial mass parameter improves the efficiency of the Car-Parrinello and Ehrenfest molecular dynamics.