Anisotropic molecular coarse-graining by force and torque matching with neural networks
| dc.contributor.author | Wilson, M.O. | |
| dc.contributor.author | Huang, D.M. | |
| dc.date.issued | 2023 | |
| dc.description.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. | |
| dc.description.statementofresponsibility | Marltan O. Wilson and David M. Huang | |
| dc.identifier.citation | Journal of Chemical Physics, 2023; 159(2):024110-1-024110-15 | |
| dc.identifier.doi | 10.1063/5.0143724 | |
| dc.identifier.issn | 0021-9606 | |
| dc.identifier.issn | 1089-7690 | |
| dc.identifier.orcid | Huang, D.M. [0000-0003-2048-4500] | |
| dc.identifier.uri | https://hdl.handle.net/2440/139156 | |
| dc.language.iso | en | |
| dc.publisher | AIP Publishing | |
| dc.relation.grant | http://purl.org/au-research/grants/arc/DP190102100 | |
| dc.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 | |
| dc.source.uri | https://doi.org/10.1063/5.0143724 | |
| dc.subject | Organic semiconductors; Phase transitions; Anisotropic interactions; Artificial neural networks; Machine learning; Many body problems; Coarse-grain model; Classical statistical mechanics | |
| dc.title | Anisotropic molecular coarse-graining by force and torque matching with neural networks | |
| dc.type | Journal article | |
| pubs.publication-status | Published |