Machines learn to unearth new materials

Zachary Ulissi (right) explores how surface chirality affects chemical reactions.Credit: Materials Science and Engineering Department/Carnegie Mellon University Materials scientists are increasingly turning to machine learning and other computational techniques to discover new materials. From corrosion resistant aeroplane components and better batteries to new drugs or novel catalysts, big data can help to find them. "The problem is that the number of possible materials is infinite," says Matthias Scheffler, a computational materials scientist at the Fritz-Haber Institute in Berlin, Germany. "With high-throughput screening, you can screen thousands of systems, and a thousand is nothing compared to infinite." Along with physicist Claudia Draxl, of Humboldt University Berlin, Scheffler launched the Novel Materials Discovery Laboratory (NOMAD) at Fritz-Haber, a data repository for a wide variety of information about chemical compounds.