To help combat climate change, many car manufacturers are racing to add more electric vehicles in their lineups. But to convince prospective buyers, manufacturers need to improve how far these cars can go on a single charge. Figuring out how to make extremely powerful but lightweight batteries. Typically, however, it takes decades for scientists to thoroughly test new battery materials, says Pablo Leon, an MIT graduate student in materials science. To accelerate this process, Leon is developing a machine-learning tool for scientists to automate one of the most time-consuming, yet key, steps in evaluating battery materials.

Electric vehicles have the potential to substantially reduce carbon emissions, but car companies are running out of materials to make batteries. One crucial component, nickel, is projected to cause supply shortages as early as the end of this year. Scientists recently discovered four new materials that could potentially help--and what may be even more intriguing is how they found these materials: the researchers relied on artificial intelligence to pick out useful chemicals test from a list of more than 300 options. And they are not the only humans turning to A.I. for scientific inspiration. Creating hypotheses has long been a purely human domain.

New battery materials are constantly being invented, but there are still challenges in producing them at a large scale and at high quality. Through the power of artificial intelligence (AI) and advanced simulation, scientists can dramatically accelerate translating these materials from benchtop to large-scale manufacturing and in the process provide a way to generate higher-performance materials at scale. Argonne researchers are currently using AI to optimize nanomaterials produced from flame-spray pyrolysis (FSP) in a minimum number of trials. Argonne scientists are simultaneously building a comprehensive simulation of FSP to reveal the physics and inform the AI model. An advanced suite of diagnostics available at the FSP facility will provide validation data for the simulations.

Toyota has turned to artificial intelligence for help in the hunt for new advanced battery materials and fuel cell catalysts. The Toyota Research Institute (TRI) is investing $35 million into the project and is teaming up with various institutions and companies, including MIT and Stanford University. By using artificial intelligence techniques, such as machine learning, the researchers can reduce the time it takes to conjure up new materials it wants to use for future zero-emission and carbon-neutral vehicles.

Toyota has turned to artificial intelligence for help in the hunt for new advanced battery materials and fuel cell catalysts. The Toyota Research Institute (TRI) is investing $35 million into the project and is teaming up with various institutions and companies, including MIT and Stanford University. By using artificial intelligence techniques, such as machine learning, the researchers can reduce the time it takes to conjure up new materials it wants to use for future zero-emission and carbon-neutral vehicles. "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."