Data-Driven Parametrization of All-Atom Force Fields for Organic Semiconductors

Organic semiconductors (OSCs) composed of π conjugated molecules have attracted significant interest in studying bulk properties such as molecular arrangements and electron mobility. However, current traditional force fields (FFs) offer limited torsion types, failing to cover the full chemical space of π conjugated molecules and hindering further molecular dynamics simulation in deducing bulk properties through statistical mechanics. In this study, we introduce OSCFF, a GAFF2-compatible FF that supports diverse torsions for conjugated molecules and enables high-accuracy RESP charge prediction through a neural network (NN). To develop the OSCFF, we construct two expansive and diverse molecular data sets: one consists of around 56,000 fragment geometries with torsion profiles and another consists of around 472,000 optimized molecular geometries with RESP charges. Using these data sets, we train NN models to predict RESP charges and fit the missing dihedral parameters in GAFF2 through automatic differentiation techniques. We further demonstrate that OSCFF achieves high accuracy in predicting torsional energy profiles, RESP charges, and radial distribution functions for conjugated systems. Additionally, we release the data sets, dihedral parameters, and RESP model as open-source resources. We believe OSCFF will serve as a valuable tool for advancing the study of bulk properties in OSCs.