  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.
