Anisotropic molecular coarse-graining by force and torque matching with neural networks

Marltan O. Wilson; David M. Huang

doi:10.1063/5.0143724

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arXiv · 2301.10881

Anisotropic molecular coarse-graining by force and torque matching with neural networks

Marltan O. Wilson·David M. Huang

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Abstract

We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network potentials by addressing molecular anisotropy. We demonstrate the flexibility of the method by parametrizing single-site coarse-grained models of a rigid small molecule (benzene) and a semi-flexible organic semiconductor (sexithiophene), attaining structural accuracy close to the all-atom models for both molecules at considerably lower computational expense. The machine-learning method of constructing the coarse-grained potential is shown to be straightforward and sufficiently robust to capture anisotropic interactions and many-body effects. The method is validated through its ability to reproduce the structural properties of the small molecule's liquid phase and the phase transitions of the semi-flexible molecule over a wide temperature range.

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BibTeX RIS

Marltan O. Wilson, David M. Huang. 2023-01-26. Anisotropic molecular coarse-graining by force and torque matching with neural networks. https://doi.org/10.1063/5.0143724

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