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MIT teams receiving $10M from TRI for next-gen battery materials

4 April 2017

Three MIT-affiliated research teams will receive about $10M in funding as part of a $35M materials science discovery program launched by the Toyota Research Institute (TRI). (Earlier post.) Provided over four years, the support to MIT researchers will be primarily directed at scientific discoveries and advancing energy storage.

MIT’s Martin Bazant, joined by colleagues at Stanford University and Purdue University, will lead an effort to develop a novel, data-driven design of lithium-ion (Li-ion) batteries. Leveraging a nanoscale visualization technique that revealed, for the first time, how Li-ion particles charge and discharge in real time, in good agreement with his theoretical predictions, Bazant, the E. G. Roos (1944) Professor of Chemical Engineering and a professor of mathematics, will use machine learning to develop a scalable predictive modeling framework for rechargeable batteries.

By applying machine learning methods to these videos of the inner workings of rechargeable batteries—using each pixel and each frame as a measurement—we can tease out models that better fit the experimental data. The approach has the potential to unify energy materials design by connecting atomistic with macroscopic properties and advance electrochemical materials more generally.

—Prof. Bazant

In addition to Bazant’s endeavor, which also includes collaborator Richard Braatz, the Edwin R. Gilliland Professor, two other MIT-affiliated projects will receive support from TRI. Jeffrey Grossman, the Morton and Claire Goulder and Family Professor in Environmental Systems, and Yang Shao-Horn, the W.M. Keck Professor of Energy, will lead the largest funded project focused on the design principles of polymer stability and conductivity for lithium batteries.

The team also includes Jeremiah A. Johnson, the Firmenich Career Development Associate Professor in the Department of Chemistry, and Adam Willard, assistant professor in chemistry, as well as machine learning and optimization expert Suvrit Sra, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) in the Department of Electrical Engineering and Computer Science.

[The research] brings together diverse expertise and offers a remarkable opportunity to develop machine learning models tuned to the problem, as well as large-scale discrete probability and optimization algorithms, topics that lie at the heart of my research.

—Suvrit Sra

The long-term impact that machine learning, and more, broadly artificial intelligence techniques, will have on materials discovery, he adds, extends well beyond this one project. Sra expects that in addition to accelerating materials discovery the methods he develops will lead to fundamental progress in machine learning too.

In addition to these lithium battery projects, Yuriy Román, associate professor of chemical engineering, will serve as co-lead investigator with Shao-Horn to explore the design principles of nanostructured, non-precious-metal-containing catalysts for oxygen reduction and evolution.

Leveraging a novel synthesis route to create nanostructured catalysts with minute precious metals developed in the Roman lab, Roman and Shao-Horn will develop a predictive framework for catalytic activity. The researchers aim to identify new classes of stable, highly active electrocatalysts that are less expensive to produce and commercialize.

All of the research findings supported by TRI will remain open and publishable in scientific journals.

Other awards. Also funded as part of the $35-million TRI program are:

  • The University of Buffalo ($2.4 million). Krishna Rajan, ScD, Erich Bloch Endowed Chair of the Department of Materials Design and Innovation (MDI) at the UB, is the grant’s principal investigator.

  • $The University of Michigan ($2.4 million). Researchers will develop computer simulation tools to predict automotive battery performance. Initially, the program will aim to help revolutionize materials science and identify new advanced battery materials and fuel cell catalysts that can power future zero-emissions and carbon-neutral vehicles.

    The project, under the auspices of the Michigan Institute for Computational Discovery and Engineering at U-M, will combine mathematical models of the atomic nature and physics of materials with artificial intelligence. The U-M project will use the Conflux cluster, an innovative, new computing platform that enables computational simulations to interface with large datasets.

  • The University of Connecticut. UConn materials scientist Ramamurthy “Rampi” Ramprasad is leading the effort at UConn. Ramprasad’s lab will work to identify new polymers using quantum mechanical computations and data-driven machine learning. Because of their flexible chemical compositions, polymers can be used as insulators, semiconductors, and permeable membranes. They also are safe, inexpensive to produce, and light. As such, they hold the potential for broader use in energy storage applications such as rechargeable batteries and fuel cells.

  • U.K.-based materials science company Ilika.

April 4, 2017 in Batteries, Fuel Cells, Materials | Permalink | Comments (1)

Comments

Machines can see things humans miss, good approach.

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