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molecular crystalsRSC Publishing Home – Chemical Science Journals, Books and Databases
•Science
Science
80% Informative
Inspired by 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.
Benzene and glycine are used as test systems.
The obtained potentials are able to rank different benzene or glycine polymorphs in good agreement with density-functional theory.
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