Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910701942996992 |
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| author | Shinkle, Emily Pachalieva, Aleksandra Bahl, Riti Matin, Sakib Gifford, Brendan Craven, Galen T. Lubbers, Nicholas |
| author_facet | Shinkle, Emily Pachalieva, Aleksandra Bahl, Riti Matin, Sakib Gifford, Brendan Craven, Galen T. Lubbers, Nicholas |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_12112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks Shinkle, Emily Pachalieva, Aleksandra Bahl, Riti Matin, Sakib Gifford, Brendan Craven, Galen T. Lubbers, Nicholas Chemical Physics Machine Learning 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. |
| title | Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2406.12112 |