Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/139156
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Type: | Journal article |
Title: | Anisotropic molecular coarse-graining by force and torque matching with neural networks |
Author: | Wilson, M.O. Huang, D.M. |
Citation: | Journal of Chemical Physics, 2023; 159(2):024110-1-024110-15 |
Publisher: | AIP Publishing |
Issue Date: | 2023 |
ISSN: | 0021-9606 1089-7690 |
Statement of Responsibility: | Marltan O. Wilson and David M. Huang |
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. |
Keywords: | Organic semiconductors; Phase transitions; Anisotropic interactions; Artificial neural networks; Machine learning; Many body problems; Coarse-grain model; Classical statistical mechanics |
Rights: | © Author(s) 2023. . All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.1063/5.0143724 |
DOI: | 10.1063/5.0143724 |
Grant ID: | http://purl.org/au-research/grants/arc/DP190102100 |
Published version: | http://dx.doi.org/10.1063/5.0143724 |
Appears in Collections: | Chemistry and Physics publications |
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hdl_139156.pdf | Published version | 9.74 MB | Adobe PDF | View/Open |
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