About

Professor Elizabeth A. Holm leads a research group in microstructure evolution, grain boundary structure and kinetics, and microstructure informatics in the University of Michigan Department of Materials Science and Engineering.

Bio

Prior to joining U-M, Holmes spent ten years at Carnegie Mellon University, most recently as a professor of materials science and engineering. She also spent 20 years as a computational materials scientist at Sandia National Laboratories, Albuquerque, New Mexico, working on computer simulations of microstructure evolution, microcircuit aging and reliability, and the processing and welding of advanced materials. Active in a variety of professional societies, Holm is president elect designate and an honorary member of The American Institute of Mining, Metallurgical and Petroleum; a fellow of ASM International; a fellow and former president of The Minerals, Metals, and Materials Society, where she launched the TMS Summit on Creating and Sustaining Diversity; an organizer of several international conferences; and a former member of the National Materials Advisory Board. She has authored or co-authored over 160 publications.

Education

Dual PhD Materials Science and Engineering and Scientific Computing
University of Michigan

SM Ceramics
MIT

Research

Professor Holm uses the tools of computational materials science to study a variety of materials systems and phenomena. Her research areas include the theory and modeling of microstructural evolution in complex polycrystals, the physical and mechanical response of microstructures, mechanical properties of carbon nanotube networks, atomic-scale properties of internal interfaces, machine vision for automated microstructural classification, and machine learning to predict rare events. Computational techniques applied to these problems range from the atomic scale (molecular dynamics) through the mesoscale (Monte Carlo, phase field, cellular automata) to the continuum scale (finite element). A particular focus is identifying useful concepts from data science, including machine learning, machine vision, evolutionary computing, and network analysis, and developing them to answer materials science questions.