Maria Chan is an Assistant Scientist at Argonne National Laboratory. She is a Fellow at the Computation Institute of the University of Chicago. Her current research focuses on the computational modeling of materials for renewable energy, using a combination of first principles, atomistic, and machine learning techniques. She is developing new methodologies for efficient and accurate prediction of materials properties, as well as software for interfacing computational modeling with experimental materials characterization. Most recently she has been involved in the development of interatomic force fields using supervised learning techniques.
Dr. Chan obtained a PhD in Physics from MIT and BS in Physics and Mathematics from UCLA. She is currently funded by DOE and industry.