Machine learning has played an increasing role in a wide variety of scientific endeavors and materials science is no exception. From the search for, and design of, new materials to acceleration in atomistic and first principles modeling, to improvement in interpretation of operando characterization data, machine learning made significant impact which ultimately allows new understanding and discoveries.
In this talk, we will discuss the use of machine learning techniques to accelerate and enable atomistic modeling, to interpret and invert x-ray and electron-microscopy data, and ultimately to understand, improve, and design energy materials. Examples will be drawn from photovoltaic, catalytic and energy storage materials.
Maria Chan holds a B.S. in physics and applied mathematics from University of California, Los Angeles, and a Ph.D. in physics from Massachusetts Institute of Technology. Since 2012, Chan has been a staff scientist at the Center of Nanoscale Materials, part of Argonne National Laboratory near Chicago. She is also a member of the University of Chicago Consortium for Advanced Science and Engineering, and a senior fellow at the Northwestern-Argonne Institute of Science and Engineering. Chan's research is on the computational prediction of materials properties using first principles, atomistic and machine learning methods, particularly in applications towards materials relevant to energy technologies, such as energy storage, photovoltaics, catalysis and thermal management. More recently, Chan has focused on the intersection and integration between materials modeling and characterization.
For more information, please contact Vanessa Irizarry at [email protected]