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Toyota Research Institute commits $36M over four years for accelerated materials design & discovery with university partners

Toyota Research Institute commits $36M over four years for accelerated materials design & discovery with university partners
Toyota Research Institute (TRI) is committing another $36 million to its Accelerated Materials Design and Discovery (AMDD) collaborative university research program over the next four years. In early 2017, TRI announced the formation of AMDD and launched the program with the mission of using AI to find new materials for emissions free mobility.
The total scope of the initial investment was $35 million over four years with multiple university partners. The new funding builds on that initial investment and will bring closer collaboration with program partners.
Our focus on collaboration is what makes our research program unique. Rather than acting strictly as a funding source, TRI has formed deep collaborations with researchers which have led to joint publications as well as co-developed open-source data and software. This collaborative approach i ....

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Toyota Research Institute Invests $36 Million for Fuel Cell Development

autoevolution
19 May 2021, 8:44 UTC ·
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Before any technological innovation starts to be used on a large scale, first there’s the scientific research process. Science is always a few steps ahead of the game, testing possibilities that end up changing our future. And this is what Toyota is supporting through its Research Institute.
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It’s been 6 years since the Toyota Research Institute (TRI) has been established, with the purpose of using artificial intelligence to develop, among other things, automated driving technologies. In 2017, TRI embarked on its most important journey so far, by launching the Accelerated Materials Design and Discovery (AMDD) research program. Through AMDD, TRI teamed up with some of the best universities in the U.S., in order to study and develop the use of AI for the specific purpose of discovering innovative materials for emissions-free mobility. ....

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Grant to accelerate AI materials discovery and design

May 18, 2021
Cornell is partnering in a $36 million grant from the Toyota Research Institute (TRI) for its Accelerated Materials Design and Discovery (AMDD) collaborative university research program, which seeks to use artificial intelligence to discover new materials that could help achieve emissions-free driving.
Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science in the Cornell Ann S. Bowers College of Computing and Information Science, is among the lead researchers on the four-year, multi-institution grant.
“AI for scientific discovery is among the most promising but challenging areas of AI,” said Gomes, who is also director of Cornell’s Institute for Computational Sustainability. “TRI’s multidisciplinary approach enables our team to develop new AI approaches for materials discovery. We combine data-driven deep learning with knowledge-driven reasoning and optimization techniques. This approach allows us to inje ....

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Grant to accelerate AI materials discovery for emissions-free driving

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ITHACA, N.Y. - Cornell University is partnering in a $36 million grant from the Toyota Research Institute (TRI) for its Accelerated Materials Design and Discovery (AMDD) collaborative university research program, which seeks to use artificial intelligence to discover new materials that could help achieve emissions-free driving.
Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, is among the lead researchers on the four-year, multi-institution grant.
AI for scientific discovery is among the most promising but challenging areas of AI, said Gomes, who is also director of Cornell s Institute for Computational Sustainability. TRI s multidisciplinary approach enables our team to develop new AI approaches for materials discovery. We combine data-driven deep learning with knowledge-driven reasoning and optimization techniques. This approach allows us to inject scientific background knowledge into the discovery and data ....

Carla Gomes , Antoniav Nielsen , John Gregoire , California Institute Of Technology , College Of Engineering , Cornell Institute For Computational Sustainability , Cornell University , Toyota Research Institute , Accelerated Materials Design , Information Science , Brian Storey , California Institute , Cornell Bowers , கார்லா கோம்ஸ் , ஜான் கிரெகொய்ரே , கலிஃபோர்னியா நிறுவனம் ஆஃப் தொழில்நுட்பம் , கல்லூரி ஆஃப் பொறியியல் , கார்னெல் பல்கலைக்கழகம் , டொயோட்டா ஆராய்ச்சி நிறுவனம் , முடுக்கப்பட்ட பொருட்கள் வடிவமைப்பு , தகவல் அறிவியல் , பிரையன் மாடி , கலிஃபோர்னியா நிறுவனம் , கார்னெல் பந்துவீச்சாளர்கள் ,

X-Ray Experiments, Machine Learning Could Trim Years Off Battery R&D

X-Ray Experiments, Machine Learning Could Trim Years Off Battery R&D
An X-ray instrument at Berkeley Lab contributed to a battery study that used an innovative approach to machine learning to speed up the learning curve about a process that shortens the life of fast-charging lithium batteries.
Researchers used Berkeley Lab’s Advanced Light Source, a synchrotron that produces light ranging from the infrared to X-rays for dozens of simultaneous experiments, to perform a chemical imaging technique known as scanning transmission X-ray microscopy, or STXM, at a state-of-the-art ALS beamline dubbed COSMIC.
Researchers also employed “in situ” X-ray diffraction at another synchrotron – SLAC’s Stanford Synchrotron Radiation Lightsource – which attempted to recreate the conditions present in a battery, and additionally provided a many-particle battery model. All three forms of data were combined in a format to help the machine-learning algorithms learn the physic ....

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