(73a) Multiscale Electronic Coarse-Graining of Soft Materials Via Supervised Machine Learning

The computational design of soft materials with advanced electronic, ionic, or optical functionalities depends on the accurate treatment of (i) classical morphologies and (ii) quantum-mechanical electronic structure. This effort is challenging due to the multiscale nature of soft materials in which the strong coupling of disparate length and time scales mediates material performance. Traditionally, a hybrid classical and quantum-mechanical approach is employed, for which the sampling of conformational space can be demanding. Coarse-grained (CG) models can produce representative morphologies, but CG configurations must be back-mapped into atomistic representations to perform quantum-mechanical calculations, which is computationally intensive and inconsistent with the spatial resolution of the CG models.

Here, a supervised machine learning methodology, denoted as artificial neural network electronic coarse graining (ANN-ECG), is described in which the conformationally dependent electronic structure of a system is directly mapped to CG degrees of freedom. By averaging over decimated degrees of freedom, ANN-ECG accelerates simulations by orders of magnitude via eliminating backmapping and repeated quantum-chemical calculations. The approach is accurate, consistent with the CG spatial resolution, and can be used to derive physical intuition regarding collective variables that mediate electronically functional soft materials.