Issue 5, 2018

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 landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol−1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure–property relations in molecular crystal engineering.

Graphical abstract: Machine learning for the structure–energy–property landscapes of molecular crystals

Supplementary files

Article information

Article type
Edge Article
Submitted
27 Okt 2017
Accepted
11 Des 2017
First published
12 Des 2017
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2018,9, 1289-1300

Machine learning for the structure–energy–property landscapes of molecular crystals

F. Musil, S. De, J. Yang, J. E. Campbell, G. M. Day and M. Ceriotti, Chem. Sci., 2018, 9, 1289 DOI: 10.1039/C7SC04665K

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