Machines learn to unearth new materials
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 of Berlin, Scheffler launched the Novel Materials Discovery Laboratory (NOMAD) at Fritz Haber, a data repository for a wide variety of information about chemical compounds.
Draxl and Scheffler are now expanding on that with a consortium called FAIRmat, which is set to receive German federal government funding of €3 million (US$3.5 million) per year for five years, to build infrastructure that standardizes data produced by many researchers so that others can use them.
The acronym comes from the principle that data should be ‘findable, accessible, interoperable and reusable’ by any researcher, but Scheffler also views the AIR as standing for artificial-intelligence-ready.
He’d like the reams of data that chemists produce in the course of their research, much of which he says they never publish, to be collected in compatible data formats and shared so scientists can build new machine-learning models with them.