April 10 - 14, 2023
San Francisco, California
2023 MRS Spring Meeting

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. The recent developments in data-driven methods, such as machine learning and artificial intelligence, and their integration with multiscale approaches are creating 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.









Topics will include:

  • Data-driven design of new materials
  • Data-driven characterization of materials
  • Data-driven identification of constitutive relations
  • Process-structure-performance correlations
  • The development of new data-driven methods for material studies
  • Machine-learning potentials for atomistic simulations
  • Model-order reduction in multiscale computations
  • Uncertainty quantifications in multiscale computations
  • Scale-bridging methods for material studies

Invited Speakers:

  • Christos Athanasiou (Georgia Institute of Technology, USA)
  • Wei Cai (Stanford University, USA)
  • Ivano E. Castelli (Technical University of Denmark, Denmark)
  • Victor Fung (Oak Ridge National Laboratory, USA)
  • Wei Gao (Texas A&M University, USA)
  • Johann Guilleminot (Duke University, USA)
  • Ozgur Keles (San Jose State University, USA)
  • John Lambros (University of Illinois at Urbana-Champaign, USA)
  • Yen Ting Lin (Los Alamos National Laboratory, USA)
  • Nithin Mathew (Los Alamos National Laboratory, USA)
  • Shyue Ping Ong (University of California, San Diego, USA)
  • Danny Perez (Los Alamos National Laboratory, USA)
  • Brandon Runnels (University of Colorado Colorado Springs, USA)
  • Alejandro Strachan (Purdue University, USA)
  • Aidan Thompson (Sandia National Laboratories, USA)
  • Wenbin Yu (Purdue University, USA)

Symposium Organizers

Haoran Wang
Utah State University
Department of Mechanical and Aerospace Engineering
USA

Soumendu Bagchi
Los Alamos National Laboratory
Theoretical Division (T-1)
USA

Huck Beng Chew
University of Illinois at Urbana-Champaign
Department of Aerospace Engineering
USA

Jiaxin Zhang
Oak Ridge National Laboratory
Computer Science and Mathematics Division
USA

Topics

additive manufacturing artificial intelligence energy storage interface machine learning metal metamaterial multiscale polymer