😮 Google DeepMind's AI Dreamed Up 380,000 New Materials. The Next Challenge Is Making Them

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Google DeepMind's AI Dreamed Up 380,000 New Materials. The Next Challenge Is Making Them

Google DeepMind researchers say they’ve expanded the number of known stable materials tenfold. Some could be useful for everything from batteries to superconductors—if they make it out of the lab.

www.wired.com

Google DeepMind researchers say they’ve expanded the number of known stable materials tenfold. Some could be useful for everything from batteries to superconductors—if they make it out of the lab.

The robotic line cooks were deep in their recipe, toiling away in a room tightly packed with equipment. In one corner, an articulated arm selected and mixed ingredients, while another slid back and forth on a fixed track, working the ovens. A third was on plating duty, carefully shaking the contents of a crucible onto a dish. Gerbrand Ceder, a materials scientist at Lawrence Berkeley National Lab and UC Berkeley, nodded approvingly as a robotic arm delicately pinched and capped an empty plastic vial—an especially tricky task, and one of his favorites to observe. “These guys can work all night,” Ceder said, giving two of his grad students a wry look.

Stocked with ingredients like nickel oxide and lithium carbonate, the facility, called the A-Lab, is designed to make new and interesting materials, especially ones that might be useful for future battery designs. The results can be unpredictable. Even a human scientist usually gets a new recipe wrong the first time. So sometimes the robots produce a beautiful powder. Other times it’s a melted gluey mess, or it all evaporates and there’s nothing left. “At that point, the humans would have to make a decision: What do I do now?” Ceder says.

The robots are meant to do the same. They analyze what they’ve made, adjust the recipe, and try again. And again. And again. “You give them some recipes in the morning and when you come back home you might have a nice new soufflé,” says materials scientist Kristin Persson, Ceder’s close collaborator at LBNL (and also spouse). Or you might just return to a burned-up mess. “But at least tomorrow they’ll make a much better soufflé.”

Recently, the range of dishes available to Ceder’s robots has grown exponentially, thanks to an AI program developed by Google DeepMind. Called GNoME, the software was trained using data from the Materials Project, a free-to-use database of 150,000 known materials overseen by Persson. Using that information, the AI system came up with designs for 2.2 million new crystals, of which 380,000 were predicted to be stable—not likely to decompose or explode, and thus the most plausible candidates for synthesis in a lab—expanding the range of known stable materials nearly 10-fold. In a paper published today in Nature, the authors write that the next solid-state electrolyte, or solar cell materials, or high-temperature superconductor, could hide within this expanded database.


Finding those needles in the haystack starts off with actually making them, which is all the more reason to work quickly and through the night. In a recent set of experiments at LBNL, also published today in Nature, Ceder’s autonomous lab was able to create 41 of the theorized materials over 17 days, helping to validate both the AI model and the lab’s robotic techniques.


When deciding if a material can actually be made, whether by human hands or robot arms, among the first questions to ask is whether it is stable. Generally, that means that its collection of atoms are arranged into the lowest possible energy state. Otherwise, the crystal will want to become something else. For thousands of years, people have steadily added to the roster of stable materials, initially by observing those found in nature or discovering them through basic chemical intuition or accidents. More recently, candidates have been designed with computers.



The problem, according to Persson, is bias: Over time, that collective knowledge has come to favor certain familiar structures and elements. Materials scientists call this the “Edison effect,” referring to his rapid trial-and-error quest to deliver a lightbulb filament, testing thousands of types of carbon before arriving at a variety derived from bamboo. It took another decade for a Hungarian group to come up with tungsten. “He was limited by his knowledge,” Persson says. “He was biased, he was convinced.”

DeepMind’s approach is meant to look beyond those biases. The team started with 69,000 materials from Persson’s library, which is free to use and funded by the US Department of Energy. That was a good start, because the database contains the detailed energetic information needed to understand why some materials are stable and others aren’t. But it wasn’t enough data to overcome what Google DeepMind researcher Ekin Dogus Cubuk calls a “philosophical contradiction” between machine learning and empirical science. Like Edison, AI struggles to generate truly novel ideas beyond what it has seen before. “In physics, you never want to learn a thing that you already know,” he says. “You almost always want to generalize out of domain”—whether that’s to discover a different class of battery material or a new superconductivity theory.

GNoME relies on an approach called active learning. First, an AI called a graph neural network, or GNN, uses the database to learn patterns in the stable structures and figure out how to minimize the energy in the atomic bonds within new structures. Using the whole range of the periodic table, it then produces thousands of potentially stable candidates. The next step is to verify and adjust them, using a quantum mechanics technique called density-functional theory, or DFT. These refined results are then plugged back into the training data and the process is repeated.

The structures of 12 compounds in the Materials Project database.ILLUSTRATION: JENNY NUSS/BERKELEY LAB

The researchers found that, with multiple repetitions, this approach could generate more complex structures than were initially in the Materials Project data set, including some that were composed of five or six unique elements. (The data set used to train the AI largely capped out at four.) Those types of materials involve so many complex atomic interactions that they generally escape human intuition. “They were hard to find,” Cubuk says. “But now they’re not so hard to find anymore.”

But DFT is only a theoretical validation. The next step is actually making something. So Ceder’s team picked 58 crystals to create in the A-Lab. After taking into account the capabilities of the lab and available precursors, it was a random selection. And at first, as expected, the robots failed, then repeatedly adjusted their recipes. After 17 days of experiments, the A-Lab managed to produce 41 of the materials, or 71 percent, sometimes after trying a dozen different recipes.

Taylor Sparks, a materials scientist at the University of Utah who wasn’t involved in the research, says that it’s promising to see automation at work for new types of materials synthesis. But using AI to propose thousands of new hypothetical materials, and then chasing after them with automation, just isn’t practical, he adds. GNNs are becoming widely used to develop new ideas for materials, but usually researchers want to tailor their efforts to produce materials with useful properties—not blindly produce hundreds of thousands of them. “We’ve already had way too many things that we’ve wanted to investigate than we physically could,” he says. “I think the challenge is, is this scaled synthesis approaching the scale of the predictions? Not even close.”

 

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Google DeepMind AI Breakthrough Could Help Battery and Chip Development

Researchers at Google DeepMind have used AI to predict the structures of more than 2 million new materials.

time.com

Google DeepMind AI Breakthrough Could Help Battery and Chip Development​

Researchers at Google DeepMind have used artificial intelligence to predict the structures of more than 2 million new materials, in a breakthrough that could have wide-reaching benefits in sectors such as renewable energy and computing.

DeepMind published 381,000 of the 2.2 million crystal structures that it predicts to be most stable.

The breakthrough increases the number of known stable materials by a factor of ten. Although the materials will still need to be synthesized and tested, steps which can take months or even years, the latest development is expected to accelerate the discovery of new materials, which will be required for applications such as energy storage, solar cells, and superconductor chips.

“While materials play a very critical role in almost any technology, we as humanity know only about a few tens of thousands of stable materials,” says Ekin Dogus Cubuk, a Staff Research Scientist at Google Brain, who worked on the DeepMind AI tool, known as Graph Networks for Materials Exploration (GNoME). That number gets even smaller when considering which materials are suitable for specific technologies, Cubuk told journalists at a briefing on Nov. 28. “Let's say you want to find a new solid electrolyte for better batteries. These electrolytes have to be ionically good conductors but electronically bad conductors, and they should not be toxic, they should not be radioactive. Once you apply all these filters, it turns out we only have a few options that we can go with, which end up not really revolutionizing our batteries.”