AUTO INDUSTRY NEWS

Toyota invests in AI to develop next-gen fuel cells, batteries

Toyota invests in AI to develop next-gen fuel cells, batteries image

Eric Tipan / Toyota | April 05, 2017 09:54

Toyota to use AI in search of new materials for battery and fuel cell dev’t

Toyota pushes forward in search of next-generation energy for future automobiles as it launches a new project in collaboration with research entities, universities and materials science research companies.

Operating under the Toyota Research Institute (TRI), the automaker will invest US$ 35 million over the next four years in research that will use artificial intelligence (AI) in speeding up the design and discovery of advanced materials that will be used in batteries and fuel cells to power future zero-emissions and carbon-neutral vehicles.

Toyota invests in AI to develop next-gen fuel cells, batteries

“Toyota recognizes that artificial intelligence is a vital basic technology that can be leveraged across a range of industries, and we are proud to use it to expand the boundaries of materials science. Accelerating the pace of materials discovery will help lay the groundwork for the future of clean energy and bring us even closer to achieving Toyota’s vision of reducing global average new-vehicle CO2 emissions by 90 percent by 2050,” said TRI Chief Science Officer Eric Krotkov.

TRI research projects will partner 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.

Toyota invests in AI to develop next-gen fuel cells, batteries

“This represents a fantastic opportunity to drastically advance the use of databases and machine learning methods in materials discovery. The partnership combines theory, computation and experiment in an unprecedented, concerted effort. We are particularly excited by prospects for an avant-garde approach to catalyst development for fuel cells,” said Jens Norskov, Professor at Stanford University and director of the SUNCAT center.

With the help of AI, TRI hopes to reduce the time needed in new materials identification and development by merging advanced computational materials modeling, new sources of experimental data and machine learning.