MSE Virtual Seminar: Designing and Characterizing Materials with the Help of Modeling and AI/ML


3:00 pm-4:00 pm
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Maria Chan, Ph.D.

Staff Scientist
Argonne National Laboratory

Dr. Maria Chan obtained her BSc in Physics and Applied Mathematics from the University of California, Los Angeles, and Ph.D. 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. Dr. Chan’s research focuses on the computational prediction of materials properties, using first principles, atomistic, and machine learning methods, particularly in applications towards materials relevant to energy technologies. She also works on the integration of experimental characterization and computational modeling using artificial intelligence and machine learning approaches.


In materials and chemical science, the combination of high throughput computational modeling and experimentation has given rise to significant challenges and opportunities. Data science techniques such as machine learning, artificial intelligence, and computer vision have made a significant impact on the ease, scope, and speed of understanding of known materials and the discovery of new ones.

In this talk, we will discuss how we use data science approaches in conjunction with theory-based modeling to interpret experimental characterization data (such as x-ray scattering, spectroscopy, scanning probe microscopy, and electron microscopy) and carry out materials design (such as in the space of classic zinc blende and hybrid perovskite optoelectronic materials). The role of computer vision and pattern recognition in the analysis of microscopy data will also be discussed.


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Department of Materials Science & Engineering