Singh, Akksay, Wang, Jiaqi, Henkelman, Graeme, and Li, Lei. Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization. Retrieved from https://par.nsf.gov/biblio/10566098. Journal of Chemical Theory and Computation 20.22 Web. doi:10.1021/acs.jctc.4c00953.

Singh, Akksay, Wang, Jiaqi, Henkelman, Graeme, & Li, Lei. Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization. Journal of Chemical Theory and Computation, 20 (22). Retrieved from https://par.nsf.gov/biblio/10566098. https://doi.org/10.1021/acs.jctc.4c00953

Singh, Akksay, Wang, Jiaqi, Henkelman, Graeme, and Li, Lei. "Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization". Journal of Chemical Theory and Computation 20 (22). Country unknown/Code not available: American Chemical Society. https://doi.org/10.1021/acs.jctc.4c00953. https://par.nsf.gov/biblio/10566098.

@article{osti_10566098,
place = {Country unknown/Code not available}, title = {Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization}, url = {https://par.nsf.gov/biblio/10566098}, DOI = {10.1021/acs.jctc.4c00953}, abstractNote = {}, journal = {Journal of Chemical Theory and Computation}, volume = {20}, number = {22}, publisher = {American Chemical Society}, author = {Singh, Akksay and Wang, Jiaqi and Henkelman, Graeme and Li, Lei}, }