Teams Develop New AI-driven Method to Discover, Design and Develop Next Generation Materials to Help Reduce Carbon Emissions

Toyota Research Institute (TRI) and Northwestern University are collaborating to help accelerate new materials discovery, design and development with the world’s first nanomaterial “data factory.” This AI-driven methodology goes far beyond the traditional trial and error by exploring vast parameter sets, collecting data and then empowering AI to search the materials genome to find the best materials for a given application.

TRI and Northwestern developed a machine learning algorithm capable of synthesizing materials at record speeds to sift through Northwestern’s new Megalibrary — a library containing more new inorganic materials than scientists have ever collected and categorized. Together, these concepts create the first nanomaterial data factory — a groundbreaking effort to create and mine large sets of high-quality, complex first-party data. The team is using this new approach to find catalysts that can be used instead of expensive, rare materials the world currently depends on, such as platinum and iridium.

While the first application of the data factory will be used to discover new catalysts to make fuel cell vehicles more efficient, TRI and Northwestern believe this method of materials discovery will have wide-ranging applications in the future, such as clean hydrogen production, CO2 removal from air and high-efficiency solar cells.