2018 MRS Fall Meeting Home

Symposium GI01—Machine Learning and Data-Driven Materials Development and Design

Materials are an important contributor to technological progress, and yet the process of materials discovery and development has historically been inefficient. In general, the current innovation workflow is human-centered, where researchers design, conduct, analyze and interpret results obtained through experiments, simulations or literature review. Such results are often high-dimensional, large in number and heterogeneous in nature, which hinders a researcher’s ability to draw insight from this data manually. Efforts such as the Materials Genome Initiative address this issue through data-driven techniques, increasingly utilizing methods from statistics, machine learning and artificial intelligence to interpret and model materials data. Such techniques have allowed researchers to make predictions of materials structures and properties, perform autonomous research with robot scientists, and speed up computational work. The result is an acceleration of materials discovery, and a strengthened ability to draw scientific insights from complex data. This symposium will explore the synthesis of machine learning with materials research, highlighting a broad spectrum of topics in which machine learning, artificial intelligence, or statistics play a significant role in addressing problems in experimental and theoretical materials science. It will also spur discussion on the fundamental connection between machine learning and material science, and its future application and impact. The combination of machine learning with materials science has broad implications for the future of research processes, the roles of humans and intelligent systems in a coordinated human-machine partnership, and ultimately the rate at which materials research and scientific understanding progresses.

Topics will include:

  • Closed-loop, fully autonomous experimental planning and research
  • Computational materials augmented with machine learning
  • Uncertainty quantification, prior formation and Bayesian methods in materials research
  • Synthesis of physics-oriented models, knowledge, and constraints with statistical and machine learning techniques
  • Automated or computer-assisted learning of physical models and basic scientific discovery
  • Optimal experimental design under uncertainty
  • High performance computing and big data approaches to machine learning-driven materials development
  • Materials knowledge representation, ontologies and artificial intelligence
  • Human-machine interfaces and interactions in materials research
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers:

  • Leonardo Ajdelsztajn (General Electric, USA)
  • Placidus Amama (Kansas State University, USA)
  • Kieron Burke (University of California, Irvine, USA)
  • Lucy Colwell (University of Cambridge, United Kingdom)
  • Yu Ding (Texas A&M University, USA)
  • R. Bruce von Dover (Cornell University, USA)
  • A. John Hart (Massachusetts Institute of Technology, USA)
  • Ross King (University of Manchester, United Kingdom)
  • Michael Krein (Lockheed Martin, USA)
  • A. Gilad Kusne (National Institute of Standards and Technology, USA)
  • Anatole von Lilienfeld (University of Basel, Switzerland)
  • Turab Lookman (Los Alamos National Laboratory, USA)
  • Noa Marom (Carnegie Mellon University, USA)
  • Benji Maruyama (Air Force Research Laboratory, USA)
  • Marco Buongiorno Nardelli (University of North Texas, USA)
  • Juan de Pablo (University of Chicago, USA)
  • Ruth Pachter (Air Force Research Laboratory, USA)
  • Kristin Persson (Lawrence Berkeley National Laboratory, USA)
  • Krishna Rajan (University at Buffalo, USA)
  • Patrick Riley (Google Accelerated Science, USA)
  • Mark Rummeli (Leibniz Institute for Solid State and Materials Research Desden, Germany)
  • Larisa Soldatova (Goldsmiths University of London, United Kingdom)
  • Brian Storey (Toyota Research Institute, USA)
  • Olga Wodo (University at Buffalo, USA)

Symposium Organizers

Kristofer Reyes
University at Buffalo, State University of New York
USA
716-645-5159, kreyes3@buffalo.edu

John J. Boeckl
Air Force Research Laboratory
Materials and Manufacturing Directorate
USA
937-255-9906, john.boeckl@us.af.mil

Keith A. Brown
Boston University
Mechanical Engineering, Materials Science & Engineering, and Physics
USA
617-353-4841, brownka@bu.edu

Stephane Gorsse
Bordeaux INP · ENSCBP
Ecole Nationale Sup´erieure de Chimie et de Physique de Bordeaux
France

Keywords for Abstract Submission

Artificial Intelligence, Autonomy, Computation, Predictive Simulation, Robotics, Statistical Methods, Uncertainty Quantification