Transfer learning for a foundational chemistry model
Published version
Peer-reviewed
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Change log
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Abstract
Harnessing knowledge from crystal structures yields a model that can predict a variety of chemistry-relevant outcomes.
Description
Acknowledgements: I would like to acknowledge Dr Oliver P. King-Smith, Dr Alpha A. Lee, Dr Aaron Trowbridge, Dr Giacomo E. M. Crisenza, Prof. Jordi Burés, Prof. Igor Larrosa, and Prof. David Leigh for their insightful comments and discussions. Additionally, I am grateful to SmartR AI for access to their servers and the Royal Society for financial support through a Newton International Fellowship.
Keywords
Journal Title
Chemical Science
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Journal ISSN
2041-6520
2041-6539
2041-6539
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Publisher
Royal Society of Chemistry (RSC)
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Except where otherwised noted, this item's license is described as http://creativecommons.org/licenses/by/3.0/
Sponsorship
Royal Society (Newton International Fellowship)