High Order Path Integrals Made Easy

TL;DR

This paper introduces an efficient molecular dynamics method that enables the practical use of high-order path integrals for quantum nuclear effects, demonstrated on water and ice with neural-network potentials.

Contribution

The authors develop a cost-effective scheme that overcomes technical challenges, allowing high-order path integrals to be used in condensed-phase quantum simulations.

Findings

Enhanced convergence in quantum simulations of water and ice.

Compatibility with neural-network potentials for accurate modeling.

Reduced computational overhead compared to traditional methods.

Abstract

The precise description of quantum nuclear fluctuations in atomistic modelling is possible by employing path integral techniques, which involve a considerable computational overhead due to the need of simulating multiple replicas of the system. Many approaches have been suggested to reduce the required number of replicas. Among these, high-order factorizations of the Boltzmann operator are particularly attractive for high-precision and low-temperature scenarios. Unfortunately, to date several technical challenges have prevented a widespread use of these approaches to study nuclear quantum effects in condensed-phase systems. Here we introduce an inexpensive molecular dynamics scheme that overcomes these limitations, thus making it possible to exploit the improved convergence of high-order path integrals without having to sacrifice the stability, convenience and flexibility of conventional second-order techniques. The capabilities of the method are demonstrated by simulations of liquid water and ice, as described by a neural-network potential fitted to dispersion-corrected hybrid density functional theory calculations.