Accelerating structure prediction of molecular crystals using actively trained moment tensor potential

Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of inorganic crystals, we present a methodology that exploits moment tensor potentials (MTP) and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. The obtained potentials are able to rank different benzene and glycine polymorphs in good agreement with density-functional theory. Hence, we argue that MTP can be used to accelerate the computationally guided polymorph search.