Anisotropic molecular coarse-graining by force and torque matching with neural networks
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 a 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.
- Publication:
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Journal of Chemical Physics
- Pub Date:
- July 2023
- DOI:
- 10.1063/5.0143724
- arXiv:
- arXiv:2301.10881
- Bibcode:
- 2023JChPh.159b4110W
- Keywords:
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- Condensed Matter - Statistical Mechanics;
- Physics - Computational Physics
- E-Print:
- 13 pages + 8 pages supplementary material, 13 figures