Imagine a cookbook with 150,000 tempting dishes-but few recipes for making them. That's the challenge facing an effort at the Lawrence Berkeley National Laboratory (LBNL) known as the Materials Project. It has used computers to predict some 150,000 new materials that could improve devices such as battery electrodes and catalysts. But the database's users around the globe have managed to make just a fraction of these for testing, leaving thousands untried. "Synthesis has become the bottleneck," says Gerbrand Ceder, a materials scientist at LBNL. Now, Ceder and his colleagues have married artificial intelligence (AI) and robotics to eliminate that bottleneck. The AI system makes a best guess at a recipe for a desired material and then iterates the reaction conditions as robots try to create physical samples. The new setup, known as the A-Lab, is already synthesizing about 100 times more new materials per day than humans in the lab can manage. "This is the way to go," says Ali Coskun, a chemist at the University of Freiburg who isn't involved with the A-Lab, but attended the Materials Research Society meeting here last week, where the new AI approach was announced.
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