Join us in San Francisco for the 2023 MRS Spring Meeting, the 50th Anniversary of MRS. We are inviting abstract submissions to Symposium MD02-Data-Driven Multiscale Studies of Materials—Computations and Experiments. Deadline October 27, 2022
Symposium MD02-Data-Driven Multiscale Studies of Materials—Computations and Experiments
Multiscale methods have been widely used in material studies, allowing us to gain insights into material behaviors at quantum, atomistic, micro-, meso- and macro-scales. Recent developments in data-driven methods, such as machine learning and artificial intelligence, and their integration with multiscale approaches create new research opportunities. Data-driven multiscale studies of materials have shown promising results in developing interatomic potentials for atomistic modeling, designing new materials, discovering new constitutive laws, identifying processing-structure-performance correlations, and analyzing microscopy images, among many others. In this symposium, we will include the new developments of data-driven methods in computational and experimental studies of materials, the data-driven studies crossing different scales, the studies bridging computations and experiments, and the new understandings of material behaviors enabled by the data-driven multiscale methods. This symposium will bring together researchers from a broad spectrum of disciplines with a data- or multiscale-relevant component in their research to exchange research progress and inspire new research ideas.
Symposium Organizers:
Haoran Wang
Utah State University
Department of Mechanical and Aerospace Engineering
haoran.wang [at] usu.edu
Soumendu Bagchi
Los Alamos National Laboratory
sbagchi [at] lanl.gov
Huck Beng Chew
University of Illinois at Urbana-Champaign
Department of Aerospace Engineering
hbchew [at] illinois.edu
Jiaxin Zhang
Oak Ridge National Laboratory
Computer Science and Mathematics Division
zhangj [at] ornl.gov
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