One class classification as a practical approach for accelerating π–π co-crystal discovery |
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| Authors: | Aikaterini Vriza Angelos B. Canaj Rebecca Vismara Laurence J. Kershaw Cook Troy D. Manning Michael W. Gaultois Peter A. Wood Vitaliy Kurlin Neil Berry Matthew S. Dyer Matthew J. Rosseinsky |
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| Affiliation: | Department of Chemistry and Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY UK.; Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Oxford Street, Liverpool L7 3NY UK ; Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ UK ; Materials Innovation Factory, Computer Science Department, University of Liverpool, Liverpool L69 3BX UK |
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| Abstract: | 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-6H-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. |
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