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 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.
The number of recipes is essentially infinite, Ceder says. Although computers can predict which final compounds should lead to better devices, “there is no theory for synthesis that tells us what can and cannot be made,” says Kristin Persson, who heads LBNL’s Materials Project and announced the new A-Lab.
After the baking, a gumball-like dispenser adds a ball bearing to each crucible and shakes it to grind the new substance into a fine powder that’s loaded onto a slide. A robot arm then grabs each sample and slides it into an x-ray machine or other equipment for analysis. Results are fed back into the Materials Project database of materials structures and properties, and if the outcome isn’t what was predicted, the AI setup iterates the reaction conditions and starts anew.