Sheng, Hongyuan http://orcid.org/0000-0002-0494-4418
Sun, Jingwen http://orcid.org/0000-0003-3657-7672
Rodríguez, Oliver
Hoar, Benjamin B.
Zhang, Weitong http://orcid.org/0000-0003-4731-9986
Xiang, Danlei
Tang, Tianhua
Hazra, Avijit http://orcid.org/0000-0002-1102-8581
Min, Daniel S.
Doyle, Abigail G. http://orcid.org/0000-0002-6641-0833
Sigman, Matthew S. http://orcid.org/0000-0002-5746-8830
Costentin, Cyrille http://orcid.org/0000-0002-7098-3132
Gu, Quanquan http://orcid.org/0000-0001-9830-793X
Rodríguez-López, Joaquín http://orcid.org/0000-0003-4346-4668
Liu, Chong http://orcid.org/0000-0001-5546-3852
Funding for this research was provided by:
National Science Foundation (CHE-2140762, CHE-2102266, CHE-2002158)
DOE | SC | Basic Energy Sciences (Joint Center for Energy Storage Research (JCESR))
Agence Nationale de la Recherche (Labex ARCANE, CBH-EUR-GS, ANR-17-EURE-0003)
Article History
Received: 19 October 2023
Accepted: 18 March 2024
First Online: 30 March 2024
Competing interests
: B.B.H., W.Z., Q.G., and C.L. are the inventors of a patent application (PCT/US2023/068008) for the deep-learning model used in this work. The remaining authors declare no competing interests.