2019 MRS Fall Meeting & Exhibit

Call for Papers

Symposium MT02—Closing the Loop—Using Machine Learning in High-Throughput Discovery of New Materials

Automation of high-throughput experiments and computations has resulted in the creation of materials data at a scale hitherto unknown. Meanwhile, 7 years of efforts within the Materials Genome Initiative have provided tremendous advances in materials data science, empowering researchers to analyze and extract knowledge from data at unprecedented rates. This has created a field at the boundary of materials science and computer science in which artificial intelligence (A.I.) is being used to drive materials research. In fact, with the prevalence of open-source machine learning platforms, a materials researcher can quickly and effectively apply A.I. to their research. Initial efforts have already led to reports of ten to thousand-fold acceleration in the rate of materials discovery, especially when these approaches are augmented with high-throughput approaches. However, most of the general materials community lacks formal computer science training. This leads to concerns about the uniformed application of A.I. and hinders community acceptance of these powerful tools potentially resulting in a slower discovery of important materials. This symposium will provide a platform for machine learning materials science early adopters, materials data platform creators, and cutting-edge computer scientists to present their recent work exploring the interface between computer science and materials science.

Topics will include:

  • Define and highlight the current state of the art in artificial intelligence assisted materials science.
  • Enable a discussion of how physical laws can be used as constraints that allow “Big Data” algorithms to provide meaningful insights from small “deep” materials data.
  • Build mutually beneficial interactions with computer scientists
  • Highlight the emergence of autonomous “closed-loop” systems which merge automated experimental and theoretical studies with artificial intelligence
  • Materials knowledge representation, ontologies, and artificial intelligence
  • Uncertainty quantification, prior formation, and Bayesian methods in materials research
  • High-throughput experimental and computational approaches to novel materials discovery
  • Materials by Design
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers:

  • Alan Aspuru-Guzik (University of Toronto, Germany)
  • Lee Cronin (University of Glasgow, United Kingdom)
  • Gabor Csanyi (University of Cambridge, United Kingdom)
  • Brian DeCost (National Institute of Standards and Technology, USA)
  • Roman Garnett (Washington University in St. Loius, USA)
  • Carla Gomes (Cornell University, USA)
  • John Gregoire (California Institute of Technology, USA)
  • Jason Hein (University of British Columbia, Canada)
  • Olexandr Isayev (University of North Carolina, USA)
  • Heather Kulik (Massachusetts Institute of Technology, USA)
  • Jatin Kumar (Nanyang Technological University, Singapore)
  • Anatole von Lilienfeld (University of Basel, Switzerland)
  • Julia Ling (Citrine, USA)
  • Benji Maruyama (Air Force Research Laboratory, USA)
  • Klaus-Robert Muller (Technische Universität Berlin, Germany)
  • Elsa Olivetti (Massachusetts Institute of Technology, USA)
  • Kristin Persson (Lawrence Berkeley National Laboratory, USA)
  • Warren Powell (Princeton University, USA)
  • Rampi Ramprasad (Georgia Institute of Technology, USA)
  • Matthias Scheffler (Fritz-Haber-Institute of the Max-Planck-Society, Germany)
  • Vladan Stevanovic (Colorado School of Mines, USA)
  • Olga Wodo (University at Buffalo, USA)
  • Yibin Xu (National Institute for Materials Science, Japan)
  • Lusann Yang (Google, USA)

Symposium Organizers

Jason R. Hattrick-Simpers

Barnabas Poczos
Carnegie Mellon University
412-251-9209, bapoczos@cs.cmu.edu

Markus Reiher
ETH Zurich

Aleksandra Vojvodic
University of Pennsylvania
215-746-2264, alevoj@seas.upenn.edu