Maria Chan
Scientist Maria Chan (CNM) will discuss her Laboratory-Directed Research and Development (LDRD) sponsored work at the LDRD Seminar Series presentation Tuesday, Oct. 31, 2017. “Atoms in 3D from 2D Images” begins at 12:30 p.m. in Building 203, Room D120. All are welcome to attend.
Abstract
Solid-solid interfaces are important for materials properties in a wide variety of systems, yet the characterization of these interfaces is challenging both via experiments and computational modeling. In particular, the interfacial region can be poorly imaged in electron microscopy, even if resolution is atomic away from the interface. In this project, we use a combination of atomistic modeling and multi-objective genetic algorithm, in conjunction with transmission electron microscopy (TEM) and image matching algorithms, to solve this problem and produce three-dimensional atomistic structures of complex solid-solid interfaces. This project combines atomistic and first principles modeling, optimization and computer vision algorithms, and electron microscopy imaging. We are also extending the approach to other experimental characterization techniques that are sensitive to atomistic structure, such as X-ray pair distribution function.
Biography
Maria Chan began as a postdoc at Argonne in 2010, and joined CNM as staff in 2012. She has conducted research primarily in the area of first principles modeling of renewable energy materials, on topics relavant to photovoltaics, energy storage, catalysis and thermal management. A particular focus involves developments which improve and accelerate materials understanding through combining experimental and atomistic modeling information, making use of machine learning algorithms where appropriate. In addition, she also focuses on improvements in first principles prediction of materials properties and processes, including accurate simulations of X-ray, electron, and scanning probe microscopy signals.
Chan is a fellow of the Computation Institute at the University of Chicago. She obtained her Ph.D. in physics at the Massachusetts Institute of Technology.