Capturing 3D microstructures in real time

Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE's Argonne National Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry. "What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions," said Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. "For example, with data analyzed by our 3D tool," said Henry Chan, CNM postdoctoral researcher and lead author of the study, "users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials." Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains.