Machine learning workflows to predict crystallisability, glass forming ability, mechanical properties of small organic compounds
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Srirambhatla, Vijay K and Johnston, Blair and Florence, Alastair (2022) Machine learning workflows to predict crystallisability, glass forming ability, mechanical properties of small organic compounds. In: CMAC Annual Open Day 2022, 2022-05-16 - 2022-05-18.
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ORCID iDs
Srirambhatla, Vijay K
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Persistent Identifier
https://doi.org/10.17868/strath.00081660-
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Item type: Conference or Workshop Item(Poster) ID code: 81660 Dates: DateEvent16 May 2022PublishedSubjects: Medicine > Therapeutics. Pharmacology Department: Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
Strategic Research Themes > Advanced Manufacturing and Materials
Technology and Innovation Centre > Continuous Manufacturing and Crystallisation (CMAC)Depositing user: Pure Administrator Date deposited: 02 Aug 2022 15:26 Last modified: 04 Dec 2024 01:37 URI: https://strathprints.strath.ac.uk/id/eprint/81660
CORE (COnnecting REpositories)