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

• URL: http://arxiv.org/abs/2406.12112v1
• Date: Mon, 17 Jun 2024 21:44:05 GMT
• Title: Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
• Authors: Emily Shinkle, Aleksandra Pachalieva, Riti Bahl, Sakib Matin, Brendan Gifford, Galen T. Craven, Nicholas Lubbers,
• Abstract summary: We use a graph-convolutional neural network architecture to develop a highly automated training pipeline for coarse grained force fields. We show that this approach yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions.
• Score: 36.136619420474766
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 to 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 not only yields 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.

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