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

Kevlishvili, Ilia; St. Michel, Roland G.; Garrison, Aaron G.; Toney, Jacob W.; Adamji, Husain; Jia, Haojun; Román-Leshkov, Yuriy; Kulik, Heather J.

doi:10.1039/D4FD00087K

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

Journal Article · 13 January 2025 · Faraday Discussions

DOI: https://doi.org/10.1039/D4FD00087K · OSTI ID:2447510

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Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Leveraging natural language processing models including transformers, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0016214

OSTI ID:: 2447510

Journal Information:: Faraday Discussions, Journal Name: Faraday Discussions Vol. 256; ISSN 1359-6640; ISSN FDISE6

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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