Georgescu, Alexandru B., Ren, Peiwen, Toland, Aubrey R., Zhang, Shengtong, Miller, Kyle D., Apley, Daniel W., Olivetti, Elsa A., Wagner, Nicholas, and Rondinelli, James M. Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds. Chemistry of Materials 33.14 Web. doi:10.1021/acs.chemmater.1c00905.

Georgescu, Alexandru B., Ren, Peiwen, Toland, Aubrey R., Zhang, Shengtong, Miller, Kyle D., Apley, Daniel W., Olivetti, Elsa A., Wagner, Nicholas, & Rondinelli, James M. Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds. Chemistry of Materials, 33 (14). https://doi.org/10.1021/acs.chemmater.1c00905

Georgescu, Alexandru B., Ren, Peiwen, Toland, Aubrey R., Zhang, Shengtong, Miller, Kyle D., Apley, Daniel W., Olivetti, Elsa A., Wagner, Nicholas, and Rondinelli, James M. "Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds". Chemistry of Materials 33 (14). Country unknown/Code not available: American Chemical Society. https://doi.org/10.1021/acs.chemmater.1c00905. https://par.nsf.gov/biblio/10409534.

@article{osti_10409534,
place = {Country unknown/Code not available}, title = {Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds}, url = {https://par.nsf.gov/biblio/10409534}, DOI = {10.1021/acs.chemmater.1c00905}, abstractNote = {Not Available}, journal = {Chemistry of Materials}, volume = {33}, number = {14}, publisher = {American Chemical Society}, author = {Georgescu, Alexandru B. and Ren, Peiwen and Toland, Aubrey R. and Zhang, Shengtong and Miller, Kyle D. and Apley, Daniel W. and Olivetti, Elsa A. and Wagner, Nicholas and Rondinelli, James M.}, }