When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to streamline the discovery process using a machine learning system. As a demonstration, the team arrived at a set of the eight most promising materials, out of nearly 3 million candidates, for a flow battery. This culling process would have taken 50 years by conventional analytical methods, they say, but they accomplished it in five weeks. The findings are reported in an open-access paper in the journal ACS Central Science . The study looked at a set of materials called transition metal complexes. These can exist in a vast number of different forms. MIT professor of chemical engineering Heather […]