Maria Chan
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.[4] She has a bachelor's degree in physics and applied mathematics from the University of California, Los Angeles, and a Ph.D. in physics from the Massachusetts Institute of Technology.[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.[5] She joined the Argonne National Laboratory as a postdoctoral researcher before continuing there as a staff researcher.[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".[6]
References
[edit]- ^ a b "Maria K. Chan", Profiles, Argonne National Laboratory, retrieved 2024-12-01
- ^ Monroe, Don (March 2023), "Artificial Intelligence for Materials Discovery", Communications of the ACM, 66 (4): 9–11, doi:10.1145/3583080
- ^ Mitchem, Savannah; Fitzpatrick, Mary (June 23, 2020), "Six Argonne researchers receive DOE Early Career Research Program awards", Awards and Recognition, Argonne National Laboratory, retrieved 2024-12-01
- ^ a b "Staff Spotlight – Maria Chan", Educational Programs and Outreach, Argonne National Laboratory, October 5, 2022, retrieved 2024-12-01
- ^ Chan, Maria Kai Yee (2009), Atomistic and ab initio prediction and optimization of thermoelectric and photovoltaic properties (Ph.D. thesis), Massachusetts Institute of Technology, hdl:1721.1/7582
- ^ APS Fellows archive, American Physical Society, retrieved 2024-12-01
External links
[edit]- Maria Chan publications indexed by Google Scholar
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