Workshop on Data-Driven Design of Heterogeneous Materials

Abstract

Despite significant investments in various subdomains supporting materials data and design science, there is a sizable gap in developing and integrating the solutions needed to solve the larger-scale and more complex materials design problems relevant to the Army. For example, multi-functional, reconfigurable, and autonomous soft materials systems, and heterogeneous and/or composite structural materials. The complexity and scale of these challenges mandates a need for sustained multidisciplinary effort from the materials science,chemistry, physics, mechanics, mathematics, and computer science communities, and the principled integration and deployment of data-driven design (i.e., machine learning and artificial intelligence) to accelerate materials discovery and development. The proposed joint workshop will bring together data and computer scientists with scientists working in both the structural/composite hard materials and the soft functional materials communities to identify critically pressing problems and funding gaps in the data-driven design of heterogeneous materials and prescribe potential paths forward.

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Document Details

Document Type
Technical Report
Publication Date
Apr 03, 2023
Accession Number
AD1211930

Entities

People

  • Andrew L. Ferguson
  • Juan De Pablo

Organizations

  • University of Chicago

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computer Programming
  • Computer Science
  • Computers
  • Crystal Structure
  • Data Sets
  • Machine Learning
  • Manufacturing
  • Materials
  • Materials Science
  • Molecular Dynamics
  • Network Science
  • Neural Networks
  • Reliability
  • Simulations
  • Standards

Readers

  • Defense Technology Research and Development.
  • Distributed Systems and Data Platform Development
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy