General Motors is leading a $60 million Series B financing round in Mitra Chem, a Silicon Valley-based, AI-enabled battery materials innovator. The company’s AI-powered platform and advanced research and development facility in Mountain View, California, will help accelerate GM’s commercialization of affordable electric vehicle batteries.
GM and Mitra Chem will develop advanced iron-based cathode active materials (CAM), like lithium manganese iron phosphate (LMFP), to power affordable and accessible EV batteries compatible with GM’s EV propulsion architecture, the Ultium Platform. GM’s funding will help Mitra Chem to scale its current operations and to expedite their novel battery materials formulation to market.
“This is a strategic investment that will further help reinforce GM’s efforts in EV batteries, accelerate our work on affordable battery chemistries like LMFP and support our efforts to build a U.S.-focused battery supply chain,” said Gil Golan, GM vice president, technology acceleration and commercialization. “GM is accelerating larger investments in critical subdomains of battery technology. Mitra Chem’s labs, methods and talent will fit well with our own R&D team’s work.”
Mitra Chem’s battery R&D facility can simulate, synthesize and test thousands of cathode designs monthly, ranging in size from grams to kilograms. These processes drive significantly shortened learning cycles, enabling shorter time to market for new battery cell formulas.
Mitra Chem is building the first North American lithium-ion battery materials product company that shortens the lab-to-production timeline by over 90 percent. Lithium-ion batteries are the key platform technology enabling electrification in transportation, consumer electronics, along with residential, commercial, and grid-scale energy storage. Mitra Chem’s first product category is iron-based cathodes for Western battery applications. Iron-based cathodes shift away from the use of elements such as nickel and cobalt. Mitra Chem takes cathode products from lab to industrial scale faster than the competition by leveraging an in-house machine learning technology advantage to dramatically shorten the R&D timeline. The company’s goal is to transform the cathode from a specialty chemical to a platform technology that differentiates cell performance by end application.
