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Machine Learning for Accelerated Metal Oxide Thermochemical Predictions.

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Title: Machine Learning for Accelerated Metal Oxide Thermochemical Predictions.
Authors: Joyner NA; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States., Sprouse S; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States., Herndon H; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States., Butler KE; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States., Kaye E; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States., Dixon DA; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States., Makoś MZ; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.
Source: Journal of chemical theory and computation [J Chem Theory Comput] 2026 Mar 10; Vol. 22 (5), pp. 2480-2489. Date of Electronic Publication: 2026 Feb 20.
Publication Type: Journal Article
Journal Info: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101232704 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-9626 (Electronic) Linking ISSN: 15499618 NLM ISO Abbreviation: J Chem Theory Comput Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Joyner+NA%22">Joyner NA</searchLink>; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.<br /><searchLink fieldCode="AU" term="%22Sprouse+S%22">Sprouse S</searchLink>; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.<br /><searchLink fieldCode="AU" term="%22Herndon+H%22">Herndon H</searchLink>; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.<br /><searchLink fieldCode="AU" term="%22Butler+KE%22">Butler KE</searchLink>; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.<br /><searchLink fieldCode="AU" term="%22Kaye+E%22">Kaye E</searchLink>; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.<br /><searchLink fieldCode="AU" term="%22Dixon+DA%22">Dixon DA</searchLink>; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.<br /><searchLink fieldCode="AU" term="%22Makoś+MZ%22">Makoś MZ</searchLink>; Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.
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  Data: <searchLink fieldCode="JN" term="%22101232704%22">Journal of chemical theory and computation</searchLink> [J Chem Theory Comput] 2026 Mar 10; Vol. 22 (5), pp. 2480-2489. <i>Date of Electronic Publication: </i>2026 Feb 20.
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        Value: 10.1021/acs.jctc.5c01798
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      – TitleFull: Machine Learning for Accelerated Metal Oxide Thermochemical Predictions.
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              Text: 2026 Mar 10
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