Reliability Assessment for Large-Scale Molecular Dynamics Approximations

TL;DR

This paper develops and evaluates large-scale approximation methods for molecular dynamics simulations, enabling efficient and reliable modeling of key macroscopic properties despite the inherent chaos and complexity of molecular systems.

Contribution

It introduces a class of large-scale MD approximations exploiting the two-scale nature of molecular dynamics, with a focus on their reliability and accuracy.

Findings

Large-scale MD approximations accurately model energy and momentum.

The methods are computationally efficient for large systems.

Approximate models retain key macroscopic features of molecular motions.

Abstract

Molecular dynamics (MD) simulations are used in biochemistry, physics, and other fields to study the motions, thermodynamic properties, and the interactions between molecules. Computational limitations and the complexity of these problems, however, create the need for approximations to the standard MD methods and for uncertainty quantification and reliability assessment of those approximations. In this paper, we exploit the intrinsic two-scale nature of MD to construct a class of large-scale dynamics approximations. The reliability of these methods are evaluated here by measuring the differences between full, classical MD simulations and those based on these large-scale approximations. Molecular dynamics evolutions are non-linear and chaotic, so the complete details of molecular evolutions cannot be accurately predicted even using full, classical MD simulations. This paper provides numerical results that demonstrate the existence of computationally efficient large-scale MD approximations which accurately model certain large-scale properties of the molecules: the energy, the linear and angular momenta, and other macroscopic features of molecular motions.