DFT-informed Machine Learned Force Fields for Rubrene

Density Functional Theory (DFT)-informed Machine Learned Force Fields (MLFF) for the organic semiconductor rubrene are developed using Vienna Ab initio Simulation Package (VASP) and Moment Tensor Potential (MLIP package) methods. This research contributes to the growing MLFFs field, which aims to apply machine learning methods to develop force fields able to bridge the gap between the accuracy of ab initio calculations and the computational efficiency of classical force fields.