Quantum dynamics using path integral coarse-graining

TL;DR

This paper introduces a machine learning-enhanced path integral method that significantly reduces computational costs and improves accuracy in simulating quantum vibrational spectra of molecular systems.

Contribution

It presents a novel, efficient path integral approach with a temperature elevation scheme, enabling routine quantum vibrational spectra calculations with lower computational expense.

Findings

Significant computational savings over traditional methods

Dramatically improved accuracy in vibrational spectra calculations

Effective attenuation of artefacts and temperature scaling issues

Abstract

Vibrational spectra of condensed and gas-phase systems containing light nuclei are influenced by their quantum-mechanical behaviour. The quantum dynamics of light nuclei can be approximated by the imaginary time path integral (PI) formulation, but still at a large computational cost that increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop a PI method with the reduced computational cost of a classical simulation. We also propose a simple temperature elevation scheme to significantly attenuate the artefacts of standard PI approaches and also eliminate the unfavourable temperature scaling of the computational cost.We illustrate the approach, by calculating vibrational spectra using standard models of water molecules and bulk water, demonstrating significant computational savings and dramatically improved accuracy compared to more expensive reference approaches. We believe that our simple, efficient and accurate method could enable routine calculations of vibrational spectra including nuclear quantum effects for a wide range of molecular systems.