(611h) Mapping Configurationally-Dependent Electronic Structure to Coarse-Grained Models with Machine Learning

The modeling of functional soft materials involves the synthesis of multiscale simulation techniques to describe the impact of nuclear degrees of freedom on the electronic structure of the material. For these systems, configurational sampling is often a substantial computational bottleneck warranting the use of computationally-cheaper coarse-grained models. However, these coarse simulations must be followed by atomistic backmapping, as well as electronic structure calculations on each configuration, in order to accurately describe the material's electronic structure. Here, we present a simulation protocol for mapping configuration-dependent electronic structure directly to coarse-grained nuclear degrees of freedom using techniques from supervised machine learning. We describe the impact of this approach on the acceleration of simulations, as well as the potential ability to discover coarse-grained representations relevant to organic semiconductors.