2019 MRS Fall Meeting & Exhibit

Call for Papers

Symposium MT03—Automated and Data-Driven Approaches to Materials Development—Bridging the Gap Between Theory and Industry

Process development cycles of materials from discovery through synthesis towards manufacturing and devices is inherently slow (15-25 years). Today, there is parallel development of automation and robotics, high performance computing (HPC) and ubiquitous machine learning algorithms, the confluence of which, can better inform experiments and theory. This unique opportunity can potentially transform the way in which we do materials research, with faster learning, new tools and deeper insight into materials science problem. This can ultimately accelerate innovation and result in faster returns-on-investment for funding agencies and practitioners alike. Application of machine learning to materials science problems is still a nascent field, and many open questions remain. Especially intriguing is the unique nature of the material science as a testbed to test newly developed algorithms in machine learning – sparse datasets, varying qualities of labeled data and natural language processing to mine data from large bodies of literature are key features that can benefit both fields. In addition, new experimental capabilities can be built that can leverage upon the extended toolkit that machine learning and automation along with HPC can provide.
The purpose of this symposium is to convene experts in various domains with a shared interest in accelerating the rate of novel materials development. Specifically, this symposium will focus on bridging gaps between theory and industrial application, with an emphasis on the materials down-selection, accelerated testing, and industry & technology transfer.

Topics will include:

  • Materials design and search
  • First principles simulations and development of Ionization Potentials
  • Materials and molecule graph descriptors
  • Closed-loop High Throughput Experiments and Combinatorial Synthesis
  • Development of automation and robotics
  • New experimental capabilities enabled by machine learning
  • Process and synthesis optimization
  • Machine-learning with sparse data sets
  • Data management: Universal standards for data management, metadata management
  • Integration of human and machine
  • Industry developments and transfer
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers:

  • Alan Aspuru-Guzik (University of Toronto, Germany)
  • Harry Atwater (California Institute of Technology, USA)
  • Sergey Barabash (Intermolecular, Inc., USA)
  • Gerbrand Ceder (University of California, Berkeley, USA)
  • Lee Cronin (University of Glasgow, United Kingdom)
  • Brian DeCost (National Institute of Standards and Technology, USA)
  • Mohamed Eddaoudi (King Abdullah University of Science and Technology, Saudi Arabia)
  • Giulia Galli (University of Chicago, USA)
  • Carla Gomes (Cornell University, USA)
  • Anubhav Jain (Lawrence Berkeley National Laboratory, USA)
  • Yousung Jung (KAIST, Republic of Korea)
  • Jatin Kumar (Nanyang Technological University, Singapore)
  • Julia Ling (Citrine, USA)
  • Benji Maruyama (Air Force Research Laboratory, USA)
  • Apurva Mehta (SLAC, USA)
  • Shyue Ping Ong (University of California, San Diego, USA)
  • Rampi Ramprasad (Georgia Institute of Technology, USA)
  • Subramanian Sankarnarayanan (Argonne National Lab, USA)
  • Joshua Schrier (Haverford University, USA)
  • Mary Scott (UC Berkeley, USA)
  • Zack Ulissi (Carnegie Mellon University, USA)
  • Aleksandra Vojvodic (University of Pennsylvania, USA)
  • Cyrus Wadia (Nike, Inc., USA)
  • Chris Wolverton (Northwestern University, USA)
  • Andriy Zakutayev (NREL, USA)
  • Dmitry Zubarev (IBM, USA)

Symposium Organizers

Kedar Hippalgaonkar
Institute of Materials Research and Engineering
Singapore

Tonio Buonassisi
Massachusetts Institute of Technology
Mechanical Engineering
USA
+1-510-717-8413, buonassisi@mit.edu

Kristin Persson
University of California at Berkeley
Materials Science and Engineering
USA

Edward Sargent
University of Toronto
Electrical and Computer Engineering
Canada
+1-416-946-5051, ted.sargent@utoronto.ca