The trove of theoretically stable but experimentally unrealized combinations identified using an AI tool known as GNoME is more than 45 times larger than the number of such substances unearthed in the history of science, according to a paper published in Nature on Wednesday.
The number of substances found is equivalent to almost 800 years of previous experimentally acquired knowledge, DeepMind estimated, based on 28,000 stable materials being discovered during the past decade.
Two potential applications of the new compounds include inventing versatile layered materials and developing neuromorphic computing, which uses chips to mirror the workings of the human brain, Cubuk said.
The team deployed computation, historical data, and machine learning to guide an autonomous laboratory, known as the A-lab, to create 41 novel compounds from a target list of 58—a success rate of more than 70 percent.
The key to the improvements was how AI techniques were combined with existing sources such as a large data set of past synthesis reactions, he added.
The techniques outlined in the two Nature papers would enable new materials to be identified “with the speeds necessary to address the grand challenges of the world,” said Bilge Yildiz, a Massachusetts Institute of Technology professor who was not involved in either piece of research.
The original article contains 592 words, the summary contains 209 words. Saved 65%. I’m a bot and I’m open source!
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The trove of theoretically stable but experimentally unrealized combinations identified using an AI tool known as GNoME is more than 45 times larger than the number of such substances unearthed in the history of science, according to a paper published in Nature on Wednesday.
The number of substances found is equivalent to almost 800 years of previous experimentally acquired knowledge, DeepMind estimated, based on 28,000 stable materials being discovered during the past decade.
Two potential applications of the new compounds include inventing versatile layered materials and developing neuromorphic computing, which uses chips to mirror the workings of the human brain, Cubuk said.
The team deployed computation, historical data, and machine learning to guide an autonomous laboratory, known as the A-lab, to create 41 novel compounds from a target list of 58—a success rate of more than 70 percent.
The key to the improvements was how AI techniques were combined with existing sources such as a large data set of past synthesis reactions, he added.
The techniques outlined in the two Nature papers would enable new materials to be identified “with the speeds necessary to address the grand challenges of the world,” said Bilge Yildiz, a Massachusetts Institute of Technology professor who was not involved in either piece of research.
The original article contains 592 words, the summary contains 209 words. Saved 65%. I’m a bot and I’m open source!