Call for abstracts! 2023 MRS Spring Meeting MD02-Data-Driven Multiscale Studies of Materials—Computations and Experiments

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@usu.edu

Soumendu Bagchi

Los Alamos National Laboratory

sbagchi@lanl.gov

Huck Beng Chew

University of Illinois at Urbana-Champaign

Department of Aerospace Engineering

hbchew@illinois.edu

Jiaxin Zhang

Oak Ridge National Laboratory

Computer Science and Mathematics Division

zhangj@ornl.gov