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 in:Journal of chemical theory and computation 2024-12, Vol.20 (23), p.10524-10539
Main Authors: Shinkle, Emily, Pachalieva, Aleksandra, Bahl, Riti, Matin, Sakib, Gifford, Brendan, Craven, Galen T., Lubbers, Nicholas
Format: Article
Language:English
Subjects:
Alcohols
Artificial neural networks
Computer Science
Granulation
Graph neural networks
Graphical representations
Hydrocarbons
Machine learning
Molecular Mechanics
Molecular modeling
Neural networks
Tensors
Thermodynamics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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 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.
ISSN:1549-9618
1549-9626
1549-9626
DOI:10.1021/acs.jctc.4c00788