Accelerating ab initio path integral molecular dynamics with multilevel sampling of potential surface

TL;DR

This paper introduces a multilevel sampling method to accelerate ab initio path integral molecular dynamics, significantly reducing computational cost while maintaining accuracy, demonstrated on dense hydrogen and an Einstein crystal.

Contribution

The paper presents a novel multilevel sampling approach that enhances AI-PIMD efficiency by reducing ab initio evaluations without sacrificing accuracy.

Findings

Acceleration rate of 3 to 4 times with two-level implementation

Potential to reach 10 times acceleration with extrapolation

Achieved well-converged internal energy with only 16 beads

Abstract

A multilevel approach to sample the potential energy surface in a path integral formalism is proposed. The purpose is to reduce the required number of ab initio evaluations of energy and forces in ab initio path integral molecular dynamics (AI-PIMD) simulation, without compromising the overall accuracy. To validate the method, the internal energy and free energy of an Einstein crystal are calculated and compared with the analytical solutions. As a preliminary application, we assess the performance of the method in a realistic model, the FCC phase of dense atomic hydrogen, in which the calculated result shows that the acceleration rate is about 3 to 4 fold for a two-level implementation, and can be increased to 10 times if extrapolation is used. With only 16 beads used for the ab initio potential sampling, this method gives a well converged internal energy. The residual error in pressure is just about 3 GPa, whereas it is about 20 GPa for a plain AI-PIMD calculation with the same number of beads. The vibrational free energy of the FCC phase of dense hydrogen at 300 K is also calculated with an AI-PIMD thermodynamic integration method, which gives a result of about 0.51 eV/proton at a density of $r_{s}=0.912$.