Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation

Abstract

Organic molecular crystals offer a broad spectrum of potential applications. The vast number of possible molecules is both an opportunity and a challenge, because of the prohibitive expense of exhaustively searching chemical space to find novel molecules with promising solid-state properties. Computational methods can be applied to direct experimental discovery programs using high-throughput or guided searches of chemical space. However, to date, such approaches have largely focused on molecular properties, ignoring the often significant effects of the arrangement of molecules in their crystal structure on the molecule's effectiveness for the chosen application. Here, we present an evolutionary algorithm for searching chemical space that incorporates crystal structure prediction into the evaluation of candidate molecules, allowing their fitness to be evaluated based on the predicted materials' properties. As a demonstration, the crystal structure-aware evolutionary algorithm is applied here to a search space of organic molecular semiconductors, demonstrating that the inclusion of crystal structure prediction in the fitness assessment outperforms searches based on molecular properties alone in identifying molecules with high electron mobilities.

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Identifiers

ORCID for Jay Johal: orcid.org/0000-0001-8489-4803

ORCID for Graeme M. Day: orcid.org/0000-0001-8396-2771

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