In Acta crystallographica. Section A, Foundations and advances
Machine learning was employed on the experimental crystal structures of the Cambridge Structural Database (CSD) to derive an intermolecular force field for all available types of atoms (general force field). The obtained pairwise interatomic potentials of the general force field allow for the fast and accurate calculation of intermolecular Gibbs energy. The approach is based on three postulates regarding Gibbs energy: the lattice energy must be below zero, the crystal structure must be a local minimum, and, if available, the experimental and the calculated lattice energy must coincide. The parametrized general force field was then validated regarding these three conditions. First, the experimental lattice energy was compared with the calculated energies. The observed errors were found to be in the order of experimental errors. Second, Gibbs lattice energy was calculated for all structures available in the CSD. Their energy values were found to be below zero in 99.86% of the cases. Finally, 500 random structures were minimized, and the change in density and energy was examined. The mean error in the case of density was below 4.06%, and for energy it was below 5.7%. The obtained general force field calculated Gibbs lattice energies of 259 041 known crystal structures within a few hours. Since Gibbs energy defines the reaction energy, the calculated energy can be used to predict chemical-physical properties of crystals, for instance, the formation of co-crystals, polymorph stability and solubility.
Hofmann Detlef Walter Maria, Kuleshova Liudmila Nikolaevna
2023-Mar-01
Gibbs energy, crystal structure analysis, force field, intermolecular interactions, machine learning
