Wilson, M.O.Huang, D.M.2023-08-162023-08-162023Journal of Chemical Physics, 2023; 159(2):024110-1-024110-150021-96061089-7690https://hdl.handle.net/2440/139156We 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.en© 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/10.1063/5.0143724Organic semiconductors; Phase transitions; Anisotropic interactions; Artificial neural networks; Machine learning; Many body problems; Coarse-grain model; Classical statistical mechanicsJournal article10.1063/5.01437242023-08-16633630Huang, D.M. [0000-0003-2048-4500]