Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure–property relationships. Here, we employ both supervised and uns...

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Bibliographic Details
Main Authors: Kevlishvili, Ilia, St. Michel, Roland G, Garrison, Aaron G, Toney, Jacob W, Adamji, Husain, Jia, Haojun, Román-Leshkov, Yuriy, Kulik, Heather J
Other Authors: Massachusetts Institute of Technology. Department of Chemical Engineering
Format: Article
Language:English
Published: Royal Society of Chemistry 2024
Online Access:https://hdl.handle.net/1721.1/157447

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https://hdl.handle.net/1721.1/157447

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