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

Invited Speakers:

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