Dong, Rongzhi, Zhao, Yong, Song, Yuqi, Fu, Nihang, Omee, Sadman Sadeed, Dey, Sourin, Li, Qinyang, Wei, Lai, and Hu, Jianjun. DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition. Retrieved from https://par.nsf.gov/biblio/10359249. ACS Applied Materials & Interfaces 14.35 Web. doi:10.1021/acsami.2c05812.

Dong, Rongzhi, Zhao, Yong, Song, Yuqi, Fu, Nihang, Omee, Sadman Sadeed, Dey, Sourin, Li, Qinyang, Wei, Lai, & Hu, Jianjun. DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition. ACS Applied Materials & Interfaces, 14 (35). Retrieved from https://par.nsf.gov/biblio/10359249. https://doi.org/10.1021/acsami.2c05812

Dong, Rongzhi, Zhao, Yong, Song, Yuqi, Fu, Nihang, Omee, Sadman Sadeed, Dey, Sourin, Li, Qinyang, Wei, Lai, and Hu, Jianjun. "DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition". ACS Applied Materials & Interfaces 14 (35). Country unknown/Code not available. https://doi.org/10.1021/acsami.2c05812. https://par.nsf.gov/biblio/10359249.

@article{osti_10359249,
place = {Country unknown/Code not available}, title = {DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition}, url = {https://par.nsf.gov/biblio/10359249}, DOI = {10.1021/acsami.2c05812}, abstractNote = {}, journal = {ACS Applied Materials & Interfaces}, volume = {14}, number = {35}, author = {Dong, Rongzhi and Zhao, Yong and Song, Yuqi and Fu, Nihang and Omee, Sadman Sadeed and Dey, Sourin and Li, Qinyang and Wei, Lai and Hu, Jianjun}, }