Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning

TL;DR

This paper introduces a machine learning approach to incorporate thermal response forces into coarse-grained models, significantly improving their transferability across different thermodynamic conditions and enhancing the accuracy of molecular simulations.

Contribution

It presents a novel, data-efficient method for embedding temperature dependence into ML coarse-grained force-fields using thermal response forces, improving transferability.

Findings

Enhanced transferability of CG water models across thermodynamic states

Improved accuracy and predictive capability of CG dynamics

Demonstrated effectiveness of thermal response forces in ML CG models

Abstract

Machine-learned (ML) coarse-grained (CG) models are a promising tool for significantly enhancing the efficiency of molecular simulations by systematically removing degrees of freedom while retaining fidelity to the underlying fine-grained model. The CG potential of mean force (PMF) is inherently dependent on thermodynamic conditions and, hence, a CG force-field (FF) which is trained at one thermodynamic state point is not necessarily accurate at another. We propose, in this work, a novel and data-efficient means of learning temperature dependence into ML CG force-fields via training on the thermal response forces of the PMF. We demonstrate how incorporating these terms into ML CG FFs confers significantly improved transferability for CG water models and demonstrate how this transferability enables accurate and predictive CG dynamics.