The Toyota Research Institute (TRI) announced it is investing $35 million in materials science research that uses artificial intelligence and machine learning to help accelerate the design and discovery of advanced materials.
TRI will collaborate with research entities, universities and companies 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.
Initial research projects include collaborations with Stanford University, the Massachusetts Institute of Technology, the University of Michigan, the University at Buffalo, the University of Connecticut, and the U.K.-based materials science company Ilika. TRI is also in ongoing discussions with additional research partners.
ADVANCED QUANTUM MECHANICAL COMPUTATIONS AND MACHINE LEARNING
The research will merge advanced computational materials modeling, new sources of experimental data, machine learning and artificial intelligence to reduce the time scale for new materials development from a period that has historically been measured in decades.
Research programs will follow parallel paths, working to identify new materials for use in future energy systems as well as to develop tools and processes that can accelerate the design and development of new materials more broadly.
According to TRI, the initiative will focus on three key areas:
- The development of new models and materials for batteries and fuel cells;
- Broader programs to pursue novel uses of machine learning, artificial intelligence, and materials informatics approaches for the design and development of new materials; and
- New automated materials discovery systems that integrate simulation, machine learning, artificial intelligence, and/or robotics.
Ramamurthy Ramprasad, a professor in the school of engineering at the University of Connecticut,
will lead the school's effort to identify new polymers using quantum mechanical computations and data-driven machine learning.
Polymers, which possess flexible chemical compositions, are ideal for use as insulators, semiconductors and permeable membranes. Because they're also safe, inexpensive to produce, and light, Ramprasad says they hold the potential for broader use in energy storage applications such as rechargeable batteries and fuel cells.
“Given the nearly infinite chemical compositions for polymers, it is extremely likely there are new and potentially much better functional polymers out there waiting to be discovered,”
says Ramprasad. “Our goal is to accelerate the discovery process by using virtual screening methods powered by advanced computations and machine learning so that potential new polymers may be identified before they are made.”
Jens Norskov, a physicist and chemical engineering professor at Stanford University, terms the initiative "an avant-garde approach to catalyst development for fuel cells."
"This represents a fantastic opportunity to drastically advance the use of databases and machine learning methods in materials discovery," says Norskov, director of SUNCAT Center for Interface Science and Catalysis. "The partnership combines theory, computation and experiment in an unprecedented, concerted effort."
Accelerating materials science discovery represents one of four core focus areas for TRI, which was launched in 2015 with mandates to also enhance auto safety with automated technologies, increase access to mobility for those who otherwise cannot drive and help translate outdoor mobility technology into products for indoor mobility.