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, & Qian, Xiaofeng. Predicting Van der Waals Heterostructures by a Combined Machine Learning and Density Functional Theory Approach. ACS Applied Materials & Interfaces, 14 (22). Retrieved from https://par.nsf.gov/biblio/10344201. https://doi.org/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}, }