Willhelm, Daniel, Wilson, Nathan, Arroyave, Raymundo, Qian, Xiaoning, Cagin, Tahir, Pachter, Ruth, and Qian, Xiaofeng. Predicting Van der Waals Heterostructures by a Combined Machine Learning and Density Functional Theory Approach. Retrieved from https://par.nsf.gov/biblio/10344201. ACS Applied Materials & Interfaces 14.22 Web. doi:10.1021/acsami.2c04403.
Willhelm, Daniel, Wilson, Nathan, Arroyave, Raymundo, Qian, Xiaoning, Cagin, Tahir, Pachter, Ruth, and Qian, Xiaofeng.
"Predicting Van der Waals Heterostructures by a Combined Machine Learning and Density Functional Theory Approach". ACS Applied Materials & Interfaces 14 (22). Country unknown/Code not available. https://doi.org/10.1021/acsami.2c04403.https://par.nsf.gov/biblio/10344201.
@article{osti_10344201,
place = {Country unknown/Code not available},
title = {Predicting Van der Waals Heterostructures by a Combined Machine Learning and Density Functional Theory Approach},
url = {https://par.nsf.gov/biblio/10344201},
DOI = {10.1021/acsami.2c04403},
abstractNote = {},
journal = {ACS Applied Materials & Interfaces},
volume = {14},
number = {22},
author = {Willhelm, Daniel and Wilson, Nathan and Arroyave, Raymundo and Qian, Xiaoning and Cagin, Tahir and Pachter, Ruth and Qian, Xiaofeng},
}
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