Maria Kai Yee Chan is an American materials scientist at the Argonne National Laboratory.
Her research involves the applications of machine learning to nanomaterials and renewable energy, including the prediction of material properties and the retrieval of data from the published literature.
[1][2][3] Chan became interested in physics at age 11 after reading a book about relativity.
[1] Her 2009 dissertation, Atomistic and ab initio prediction and optimization of thermoelectric and photovoltaic properties, was jointly supervised by Gerbrand Ceder and John Joannopoulos.
[4] She was elected as a Fellow of the American Physical Society (APS) in 2024, after a nomination from the APS Topical Group on Energy Research and Applications, "for contributions to methodological innovations, developments, and demonstrations toward the integration of computational modeling and experimental characterization to improve the understanding and design of renewable energy materials".