Equation of state of fluid methane from first principles with machine learning potentials

TL;DR

This paper introduces a machine learning-based method to accurately simulate the equation of state of liquid methane from first principles, accounting for many-body dispersion and quantum nuclear effects.

Contribution

It develops a systematic approach using Gaussian Approximation Potentials to model the PES of molecular liquids at various accuracy levels, enabling efficient and precise predictions.

Findings

Accurate bulk density predictions across temperature and pressure ranges.

Quantum nuclear effects are essential for modeling liquid methane.

Many-body dispersion significantly influences methane's properties.

Abstract

The predictive simulation of molecular liquids requires models that are not only accurate, but computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macroscopic properties. We present a new approach to the systematic approximation of the first-principles potential energy surface (PES) of molecular liquids using the GAP (Gaussian Approximation Potential) framework. The approach allows us to create potentials at several different levels of accuracy in reproducing the true PES, which allows us to test the level of quantum chemistry that is necessary to accurately predict its macroscopic properties. We test the approach by building potentials for liquid methane (CH$_4$), which is difficult to model from first principles because its behavior is dominated by weak dispersion interactions with a significant many-body component. We find that an accurate, consistent prediction of its bulk density across a wide range of temperature and pressure requires not only many-body dispersion, but also quantum nuclear effects to be modeled accurately.