The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures.

Abstract

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 was introduced 15 years ago. Recent developments in machine learning interatomic potentials (MLIPs) have made it possible to replace computationally expensive density functional theory (DFT) calculations with AI/neural-network-based alternatives. MLIPs can reach DFT-comparable precision with a substantial gain in speed. We selected one promising MLIP, Universal Models for Atoms, trained on the Open Molecular Crystals 2025 dataset, and processed a prefiltered subset of 216 919 structures from the Cambridge Structural Database. Due to the limitations of the MLIP available when this study commenced, ionic compounds, salts and metal-containing structures were excluded. The current methodology cannot process disordered structures, and available computational resources limit the maximum unit-cell volume that can be treated to 4000 Å3. All structures in the dataset were geometry optimized using the MLIP, and similarity descriptors were calculated to quantify the differences between the original and optimized structures. Automatic analysis was followed by the manual identification of issues indicated by the descriptors' values. We detected anomalies in experimental structures that had already passed all prior validation, as well as limitations in the reliability of the MLIP PES calculations. For 1867 crystal structures, bond-pattern change was observed, while 3331 structures showed a root-mean-square Cartesian displacement greater than 0.25 Å. Future improvements to the methodology and extension to systems not covered by this study are discussed.