A Google DeepMind AI Just Discovered 380,000 New Materials. This Robot
Is Cooking Them Up.
Shelly Fan
November 30, 2023
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 chemicals, along with their properties. The results
were fed back into the AI to further hone its “recipes.”
Over many rounds, the training allowed the AI to make small mistakes. Rather
than swapping out multiple chemical structures at the same time—a
potentially catastrophic move—the AI iteratively evaluated small chemical
changes. For example, instead of replacing one chemical component with
another, it could try to only substitute half. If the swaps didn’t work, no
problem, the system weeded out any candidates that weren’t stable.
The AI eventually produced 2.2 million chemical structures, 380,000 of
which it predicted would be stable if synthesized. Over 500 of the newly
found materials were related to lithium-ion conductors, which play a critical
part in today’s batteries.
“This is like ChatGPT for materials discovery,” said Dr. Carla Gomes at Cornell
University, who was not involved in the research.
Mind to Matter
DeepMind’s AI predictions are just that: What looks good on paper may not
always work out.
Here’s where A-Lab comes in. A team led by Dr. Gerbrand Ceder at UC
Berkeley and the Lawrence Berkeley National Laboratory built an automated
robotic system directed by an AI trained on more than 30,000 published
chemical recipes. Using robotic arms, A-Lab builds new materials by picking,
mixing, and heating ingredients according to a recipe.
Over two weeks of training, A-Lab produced a string of recipes for 41 new
materials without any human input. It wasn’t a total success: 17 materials
failed to meet their mark. However, with a dash of human intervention, the
robot synthesized these materials without a hitch.
Together, the two studies open a universe of novel compounds that might
meet today’s global challenges. Next steps include adding chemical and
physical properties to the algorithm to further improve its understanding of
the physical world and synthesizing more materials for testing.
DeepMind is releasing their AI and some of its chemical recipes to the public.
Meanwhile, A-Lab is running recipes from the database and uploading their
results to the Materials Project.
To Ceder, an AI-generated map of new materials could “change the world.”
It’s not A-lab itself, he said. Rather, it’s “the knowledge and information that it
generates.”
Image Credit: Marilyn Sargent/Berkeley Lab
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