Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes
Name
d4fd00087k.pdf
Description
Published version
Size
2.7 MB
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Checksum (MD5)
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Author(s)
Kevlishvili, Ilia
St. Michel, Roland G
Garrison, Aaron G
Toney, Jacob W
Adamji, Husain
Jia, Haojun
Román-Leshkov, Yuriy
Kulik, Heather J
Date Issued
September 20, 2024
Journal
Faraday Discussions
Publisher
Royal Society of Chemistry
Citation
Kevlishvili, Ilia, St. Michel, Roland G, Garrison, Aaron G, Toney, Jacob W, Adamji, Husain et al. 2024. "Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes." Faraday Discussions.
Version
Final published version
Abstract
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 unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21 631 compounds in tmCAT, 4599 in tmPHOTO, 2782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure–property relationships with machine learning.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemistry
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Creative Commons Attribution-Noncommercial
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DOI of Published Version
https://doi.org/10.1039/d4fd00087k
