semantic_scholarIUCrJ
The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures.
TL;DR: This study detected anomalies in experimental structures that had already passed all prior validation, as well as limitations in the reliability of the MLIP PES calculations, and similarity descriptors were calculated to quantify the differences between the original and optimized structures.
A correctly solved crystal structure should agree with the experimental data, and its geometry should correspond to a local minimum on the potential energy surface (PES). The idea of verifying crystal structure solutions by comparing them with their geometry-optimized versions wa…