Tackling complexity in crystal structure determination of metal organic compounds using machine learning interatomic potentials

Abstract

We integrate ab initio crystal structure prediction with universal machine learning interatomic potentials (UMA and Orb-v3) to determine challenging metal organic compound structures. Our framework successfully resolves the previously unknown, technologically significant crystal structures of lithium phenolate, sodium cyclohexanolate, and lithium-benzimidazol-2-one, accelerating materials development.