Special Seminar

March 13, 2025

3:30 p.m. ET

Mehl Room - 2327 Wean

MaterialEyes - Seeing the Invisible using Experiment, Theory and AI

In the development of materials for realistic applications, the detection and understanding of atomic and electronic structures, especially of defects, interfaces, and nanostructures, is paramount. Advances characterization via electron, x-ray, and atom probes, in imaging, scattering, and spectroscopic modes, have brought us closer to such detection and understanding. However, such experimental data is often difficult to parse and relate to fundamental understanding of mechanisms and behavior at the atomic level. In this talk, I will discuss our efforts in MaterialEyes to use AI/ML and theoretical modeling to enable the seeing of atomic structures from experimental characterization data. I will also discuss our work in using AI to extract and use microscopy and spectroscopy data from scientific literature, and in developing data standards for materials science. 

Maria ChanMaria Chan, Center for Nanoscale Materials at Argonne National Laboratory

Chan studies nanomaterials and renewable energy materials, including solar cells, batteries, thermoelectrics, and catalysts. Her particular focus is on using artificial intelligence/machine learning (AI/ML) for interfacing modeling with x-ray, electron, and scanning probe characterization and for efficient materials property prediction. 

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