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

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...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Shinkle, Emily, Pachalieva, Aleksandra, Bahl, Riti, Matin, Sakib, Gifford, Brendan, Craven, Galen T, Lubbers, Nicholas
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.06.2024
Subjects
Artificial neural networks
Granulation
Graph neural networks
Machine learning
Neural networks
Tensors
Thermodynamics
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422