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


Vriza, Aikaterini, Canaj, Angelos B, Vismara, Rebecca, Kershaw Cook, Laurence J, Manning, Troy D ORCID: 0000-0002-7624-4306, Gaultois, Michael W ORCID: 0000-0003-2172-2507, Wood, Peter A, Kurlin, Vitaliy ORCID: 0000-0001-5328-5351, Berry, Neil ORCID: 0000-0003-1928-0738, Dyer, Matthew S ORCID: 0000-0002-4923-3003 et al (show 1 more authors) (2021) One class classification as a practical approach for accelerating π–π co-crystal discovery Chemical Science, 12 (5). pp. 1702-1719. ISSN 2041-6520, 2041-6539

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Abstract

<p>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.</p>

Item Type: Article
Uncontrolled Keywords: 34 Chemical Sciences, Machine Learning and Artificial Intelligence, Generic health relevance
Depositing User: Symplectic Admin
Date Deposited: 12 Jan 2021 09:14
Last Modified: 16 Jun 2026 07:45
DOI: 10.1039/d0sc04263c
Related Websites:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3113258
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