Vriza, Aikaterini ORCID: 0000-0002-5663-8703, Canaj, Angelos B ORCID: 0000-0002-4944-7909, Vismara, Rebecca ORCID: 0000-0001-9474-7671, 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.
Text
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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 |
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Uncontrolled Keywords: | Generic health relevance |
Depositing User: | Symplectic Admin |
Date Deposited: | 12 Jan 2021 09:14 |
Last Modified: | 14 Mar 2024 17:32 |
DOI: | 10.1039/d0sc04263c |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3113258 |