Computational understanding and design of materials for energy storage and conversion
MIE Department Seminar
November 7, 2017
11:00 AM - 12:00 PM
Computational understanding and design of materials for energy storage and conversion
Maria Chan, PhD.
Center for Nanoscale Materials, Argonne National Laboratory
Abstract: Materials properties are central to energy storage and conversion technologies, including batteries, photovoltaics, catalysis, and thermal management. Quantum mechanics-based computational approach including first principles density functional theory allows the quantitative prediction of relevant materials properties, which opens up opportunities for understanding materials properties in operando and materials design. In this talk, we will discuss our work on methodology development and application towards understanding and improving thin film photovoltaic materials, high capacity lithium battery materials, and thermal management materials. In conjunction with first principles computation, the incorporation of experimental measurements using machine learning techniques allows accelerated and improved understanding of materials behavior.
Bio: Maria Chan obtained her BSc in Physics and Applied Mathematics from the University of California, Los Angeles, and PhD in Physics from the Massachusetts Institute of Technology. Since 2012, Dr. Chan has been a staff scientist at the Center of Nanoscale Materials, part of Argonne National Laboratory near Chicago, USA. Dr. Chan's research focuses on the computational prediction of materials properties, using first principles, atomistic, and data mining methods, particularly in applications towards materials relevant to energy technologies, such as energy storage, photovoltaics, catalysis, and thermal management.
Date posted
Oct 14, 2021
Date updated
Oct 14, 2021