Arthur Y. Lin

A Generalized Approach for Incorporating Geometry and Directionality into Coarse-Grained Machine-Learned Potentials

Results show that information loss in coarse-grained modeling is governed not only by mapping resolution but also by the symmetry and geometric information retained in the representation, providing a systematic route toward more expressive and transferable coarse-grained machine-learned potentials.