One class classification as a practical approach for accelerating π-π co-crystal discovery

• Published in: Chemical science (Cambridge) Vol. 12; no. 5; pp. 172 - 1719
• Main Authors: Vriza, Aikaterini, Canaj, Angelos B, Vismara, Rebecca, Kershaw Cook, Laurence J, Manning, Troy D, Gaultois, Michael W, Wood, Peter A, Kurlin, Vitaliy, Berry, Neil, Dyer, Matthew S, Rosseinsky, Matthew J
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 08.12.2020; The Royal Society of Chemistry
• Subjects: Algorithms; Chemistry; Chemists; Classification; Cocrystallization; Crystal structure; Crystallization; Crystallography; Decision making; Machine learning; Materials selection; Polycyclic aromatic hydrocarbons; Workflow
• ISSN: 2041-6520; 2041-6539
• DOI: 10.1039/d0sc04263c

The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound polyaromatic hydrocarbon co-crystals. The two-step approach presented in this study first trains the model using all the known molecular combinations that form this class of co-crystals extracted from the Cambridge Structural Database (1722 molecular combinations), followed by scoring possible yet unknown pairs from the ZINC15 database (21 736 possible molecular combinations). Focusing on the highest-ranking pairs predicted to have higher probability of forming co-crystals, materials discovery can be accelerated by reducing the vast molecular space and directing the synthetic efforts of chemists. Further on, using interpretability techniques a more detailed understanding of the molecular properties causing co-crystallization is sought after. The applicability of the current methodology is demonstrated with the discovery of two novel co-crystals, namely pyrene-6 H -benzo[ c ]chromen-6-one ( 1 ) and pyrene-9,10-dicyanoanthracene ( 2 ). Machine learning using one class classification on a database of existing co-crystals enables the identification of co-formers which are likely to form stable co-crystals, resulting in the synthesis of two co-crystals of polyaromatic hydrocarbons.