Google DeepMind's New AI Tool Helped Create Over 700 New Materials

From EV batteries to solar cells to microchips, new materials can supercharge technological breakthroughs. But discovering them usually takes months or even years of trial-and-error research. Google DeepMind hopes to change that with a new tool that uses deep learning to dramatically speed up the process of discovering new materials. From a report: Called graphical networks for material exploration (GNoME), the technology has already been used to predict structures for 2.2 million new materials, of which more than 700 have gone on to be created in the lab and are now being tested. It is described in a paper published in Nature today. Alongside GNoME, Lawrence Berkeley National Laboratory also announced a new autonomous lab. In partnership with DeepMind, the lab takes GNoME's discoveries and uses machine learning and robotic arms to engineer new materials without the help of humans. Google DeepMind says that together, these advancements show the potential of using AI to scale up the discovery and development of new materials. GNoME can be described as AlphaFold for materials discovery, according to Ju Li, a materials science and engineering professor at the Massachusetts Institute of Technology. AlphaFold, a DeepMind AI system announced in 2020, predicts the structures of proteins with high accuracy and has since advanced biological research and drug discovery. Thanks to GNoME, the number of known stable materials has grown almost tenfold, to 421,000. "While materials play a very critical role in almost any technology, we as humanity know only a few tens of thousands of stable materials," said Dogus Cubuk, materials discovery lead at Google DeepMind, at a press briefing.

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