AI and Robot Chemist Combination Expands Materials Science

Wednesday - December 6 2023, 05:29 UTC - 11 months ago

A robot chemist has just teamed up with an AI brain to predict the properties of thousands of potential materials through a system called the A-Lab. The AI, trained heavily on the Materials Project library, found two million chemical structures and 380,000 new materials. It took the robot only 17 days to synthesize 41 of these target chemicals, a process which normally would take months and years.

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.

Let’s Talk Chemicals .

Look around you. Many things we take for granted—that smartphone screen you may be scrolling on—are based on materials chemistry. Scientists have long used trial and error to discover chemically stable structures. Like Lego blocks, these components can be built into complex materials that resist dramatic temperature changes or high pressures, allowing us to explore the world from deep sea to outer space.

Once mapped, scientists capture the crystal structures of these components and save those structures for reference. Tens of thousands are already deposited into databanks. In the new study, DeepMind took advantage of these known crystal structures. The team trained an AI system on a massive library with hundreds of thousands of materials called the Materials Project. The library includes materials we’re already familiar with and use, alongside thousands of structures with unknown but potentially useful properties.

DeepMind’s new AI trained on 20,000 known inorganic crystals—and another 28,000 promising candidates—from the Materials Project to learn what properties make a material desirable. Essentially, the AI works like a cook testing recipes: Add a little something here, change some ingredients there, and through trial-and-error, it reaches the desired results. Fed data from the dataset, it generated predictions for potentially stable new materials—a trove that could stretch tens of times the library in the Materials Project.