Anisotropic molecular coarse-graining by force and torque matching with neural networks
| Autor: | Wilson, Marltan O., Huang, David M. |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | J. Chem. Phys. 159, 024110 (2023) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1063/5.0143724 |
| Popis: | 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. Comment: 13 pages + 8 pages supplementary material, 13 figures |
| Databáze: | arXiv |
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