Naik, Richa Ramesh
Tiihonen, Armi http://orcid.org/0000-0001-9753-6802
Thapa, Janak
Batali, Clio
Liu, Zhe http://orcid.org/0000-0001-7268-6214
Sun, Shijing http://orcid.org/0000-0002-6179-1390
Buonassisi, Tonio http://orcid.org/0000-0001-8345-4937
Funding for this research was provided by:
United States Department of Defense | Defense Advanced Research Projects Agency (HR001118C0036, HR001118C0036, HR001118C0036, HR001118C0036)
U.S. Department of Energy (DE-EE0007535, DE-EE0009096, DE-EE0007535)
TOTAL SA research grant funded through MITeI Sustng Mbr 9/08
Alfred Kordelinin Säätiö
Article History
Received: 22 June 2021
Accepted: 22 February 2022
First Online: 20 April 2022
Competing interests
: Although our laboratory has IP filed covering photovoltaic technologies and materials informatics broadly, we do not envision a direct COI with this study, the content of which is open-sourced. Two of the authors (Z.L., T.B.) own equity in a startup company, Xinterra Pte Ltd, which applies machine learning to materials.