A data-driven interpretation of the stability of organic molecular crystals.

ABSTRACT: Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning of their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular build

SUBMITTER: Cersonsky RK 

PROVIDER: S-EPMC9891366 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature