A robot chemist just teamed up with an AI brain to create a trove of new materials.
Two collaborative studies from Google DeepMind and the University of California, Berkeley, describe a system that predicts the properties of new materials—including those potentially useful in batteries and solar cells—and produces them with a robotic arm.
We take everyday materials for granted: plastic cups for a holiday feast, components in our smartphones, or synthetic fibers in jackets that keep us warm when chilly winds strike.
Scientists have painstakingly discovered roughly 20,000 different types of materials that let us build anything from computer chips to puffy coats and airplane wings. Tens of thousands more potentially useful materials are in the works. Yet we’ve only scratched the surface.
The Berkeley team developed a chef-like robot that mixes and heats ingredients, automatically transforming recipes into materials. As a “taste test,” the system, dubbed the A-Lab, analyzes the chemical properties of each final product to see if it hits the mark.
Meanwhile, DeepMind’s AI dreamed up myriad recipes for the A-Lab chef to cook. It’s a hefty list. Using a popular machine learning strategy, the AI found two million chemical structures and 380,000 new stable materials—many counter to human intuition. The work is an “order-of-magnitude” expansion on the materials that we currently know, the authors wrote.
Using DeepMind’s cookbook, A-Lab ran for 17 days and synthesized 41 out of 58 target chemicals—a win that would’ve taken months, if not years, of traditional experiments.
Together, the collaboration could launch a new era of materials science. “It’s very impressive,” said Dr. Andrew Rosen at Princeton University, who was not involved in the work.
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