Data-Driven Parametrization of All-atom force fields for Organic Semiconductors

Organic semiconductors (OSCs) composed of π conjugated molecules have gained significant interest in the study of bulk properties such as molecular arrangement and electron mobility. However, the types of torsion in the traditional force field (FF) are limited, fail to cover the chemical space of π conjugated molecules, and thus, hinder further molecular dynamics (MD) simulation to deduce these bulk properties through statistical mechanics. In this study, we introduce OSCFF, which supports various types of torsion for conjugated molecules and enables the generation of RESP charges with high accuracy through the neural network (NN). To develop the OSCFF, we construct two expansive and highly diverse molecular datasets: one consists of around 56,000 fragment geometries with torsion profiles, and another consists of around 472,000 optimized molecule geometries with RESP charges. The OSCFF demonstrates high accuracy in predicting torsional energy profiles, RESP charges, and the radial distribution function (RDF) for conjugated molecule systems. Furthermore, our OSCFF is compatible with the GAFF2 and a pipeline is provided for automatically generating the Gromacs supported topology file. We expect OSCFF will reduce the manual effort required for MD simulations of OSCs and serve as a valuable tool for multiple stages of OSCs design.