Inorganic synthesis is often the bottleneck in commercializing energy materials, including batteries, solar cells, and fuel cells. By leveraging massive chemical reaction networks and selectivity algorithms, Refound's predictive platform identifies new and unconventional synthesis recipes to accelerate the discovery and optimized production of critical energy materials.
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Matthew McDermott
Matthew McDermott is the founder and CEO of Refound Materials, a materials technology company developing more efficient synthesis recipes for accelerated materials discovery. He received a B.S. in mechanical engineering from the University of Texas at Austin and a Ph.D. in materials science and engineering from UC Berkeley. Before starting Refound Materials, McDermott was a postdoctoral researcher at Lawrence Berkeley National Laboratory, developing the A-Lab, an autonomous robotic laboratory that can discover new materials.
TECHNOLOGY
Critical Need
Materials innovations drive the technological revolutions we need for ongoing global electrification efforts addressing climate change. However, designing and optimizing synthesis recipes to produce inorganic materials for batteries, solar cells, fuel cells, etc., is often a costly process of trial and error. Today, bringing a new material from the lab to market takes 10-20 years—the primary bottleneck being synthesis, including the formulation and testing of recipes. Furthermore, the lack of theoretical guidance in this process means that synthesis recipes for modern materials may be suboptimal, leading to more lengthy processing and higher production costs.
Technology Vision
Refound's predictive platform for inorganic synthesis leverages massive chemical reaction networks and selectivity models to identify the most optimal inorganic synthesis pathways to desired target materials. The expanded chemical search space and physics-based prediction allow for the hypothesis of unique and unconventional synthesis recipes often untested by researchers. Of the thousands of synthesis recipes the platform recommends for a particular target, the top-ranked routes can be downselected for tests in a conventional solid-state synthesis laboratory, significantly reducing the number of experiments typically needed in a standard trial-and-error approach.
Potential for Impact
When deployed in emerging autonomous synthesis laboratories, Refound's predictive platform will guide the design and optimization of synthesis recipes to new and existing materials, requiring just a fraction of the time typically required by human researchers. The promise is twofold: 1) accelerated commercialization of breakthrough new-energy materials and technologies, and 2) more optimal bulk manufacturing processes to produce high-purity conventional materials faster, cheaper, and more efficiently.