Fang, Zhenyao, and Yan, Qimin. Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks. Chemistry of Materials 37.4 Web. doi:10.1021/acs.chemmater.4c03028.

Fang, Zhenyao, & Yan, Qimin. Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks. Chemistry of Materials, 37 (4). https://doi.org/10.1021/acs.chemmater.4c03028

Fang, Zhenyao, and Yan, Qimin. "Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks". Chemistry of Materials 37 (4). Country unknown/Code not available: American Chemical Society. https://doi.org/10.1021/acs.chemmater.4c03028. https://par.nsf.gov/biblio/10570520.

@article{osti_10570520,
place = {Country unknown/Code not available}, title = {Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks}, url = {https://par.nsf.gov/biblio/10570520}, DOI = {10.1021/acs.chemmater.4c03028}, abstractNote = {Not Available}, journal = {Chemistry of Materials}, volume = {37}, number = {4}, publisher = {American Chemical Society}, author = {Fang, Zhenyao and Yan, Qimin}, }