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. 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.
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