Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
<|reference_start|>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 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.<|reference_end|>
@article{shinkle2024thermodynamic,
    title={Thermodynamic Transferability in Coarse-Grained Force Fields using Graph
  Neural Networks},
    author={Emily Shinkle, Aleksandra Pachalieva, Riti Bahl, Sakib Matin, Brendan
  Gifford, Galen T. Craven, and Nicholas Lubbers},
    journal={arXiv preprint arXiv:2406.12112},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12112},
    primaryClass={physics.chem-ph cs.LG}
}