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

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

  • Musil F National Center for Computational Design and Discovery of Novel Materials (MARVEL) , Laboratory of Computational Science and Modelling , Institute of Materials , Ecole Polytechnique Federale de Lausanne , Lausanne , Switzerland . Email: felix.musil@epfl.ch ; Email: sandip.de@epfl.ch.
  • De S National Center for Computational Design and Discovery of Novel Materials (MARVEL) , Laboratory of Computational Science and Modelling , Institute of Materials , Ecole Polytechnique Federale de Lausanne , Lausanne , Switzerland . Email: felix.musil@epfl.ch ; Email: sandip.de@epfl.ch.
  • Yang J School of Chemistry , University of Southampton , Highfield , Southampton , UK.
  • Campbell JE School of Chemistry , University of Southampton , Highfield , Southampton , UK.
  • Day GM School of Chemistry , University of Southampton , Highfield , Southampton , UK.
  • Ceriotti M National Center for Computational Design and Discovery of Novel Materials (MARVEL) , Laboratory of Computational Science and Modelling , Institute of Materials , Ecole Polytechnique Federale de Lausanne , Lausanne , Switzerland . Email: felix.musil@epfl.ch ; Email: sandip.de@epfl.ch.
Show more…
  • 2018-04-21
Published in:
  • Chemical science. - 2018
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/177128
Statistics
Document views: 42 File downloads: