Uncertainty Quantification in Classical Molecular Dynamics

TL;DR

This paper discusses how to quantify uncertainty in molecular dynamics simulations using ensemble methods to improve error estimates and reproducibility, enabling more reliable and actionable scientific predictions.

Contribution

It introduces a standard uncertainty quantification approach for molecular dynamics, emphasizing ensemble methods to enhance reproducibility and accuracy.

Findings

Ensemble methods provide reliable error estimates in molecular dynamics.

Chaotic nature of molecular dynamics necessitates ensemble simulations.

Application examples include materials science and ligand-protein binding.

Abstract

Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalising experimental observations, to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable the method to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand-protein binding free energy estimation.