Thermodynamic Transferability in Coarse-Grained Force Fields Using Graph Neural Networks

Journal Article · 23 November 2024 · Journal of Chemical Theory and Computation

DOI: https://doi.org/10.1021/acs.jctc.4c00788 · OSTI ID:2478566

Shinkle, Emily Suzanne  ^[1]; Pachalieva, Aleksandra Atanasova  ^[1]; Bahl, Riti ^[2]; Matin, Sakib  ^[1]; Gifford, Brendan Joel  ^[1]; Craven, Galen Thomas  ^[1]; Lubbers, Nicholas Edward  ^[1]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
2. Emory University, Atlanta, GA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared with corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse-grained force fields, which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach yields not only highly accurate force fields but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques, such as graph neural networks, to improve the construction of transferable coarse-grained force fields.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 2478566
• Report Number(s): LA-UR--24-25829
• Journal Information: Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 23 Vol. 20; ISSN 1549-9618
• Publisher: American Chemical SocietyCopyright Statement
• Country of Publication: United States
• Language: English