Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds
Name
acs.chemmater.1c00905.pdf
Description
Published version
Size
3.43 MB
Format
Adobe PDF
Checksum (MD5)
b5b391f7e78c7983099ed7538b0bd9d7
Author(s)
Georgescu, Alexandru B
Ren, Peiwen
Toland, Aubrey R
Zhang, Shengtong
Miller, Kyle D
Apley, Daniel W
Olivetti, Elsa A
Wagner, Nicholas
Rondinelli, James M
Date Issued
2021
Journal
Chemistry of Materials
Publisher
American Chemical Society (ACS)
Citation
Georgescu, Alexandru B, Ren, Peiwen, Toland, Aubrey R, Zhang, Shengtong, Miller, Kyle D et al. 2021. "Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds." Chemistry of Materials, 33 (14).
Version
Final published version
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licens
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/ACS.CHEMMATER.1C00905
