Machine learning for the structure-energy-property landscapes of molecular crystals.

ABSTRACT: Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property

SUBMITTER: Musil F 

PROVIDER: S-EPMC5887104 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature