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.
Researchers from the University of California, Berkeley and the Lawrence Berkeley National Laboratory have already used the findings as part of experimental efforts to create new materials, according to another paper published in Nature on Wednesday.
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 high success ratio was surprising and could be improved even further, said Gerbrand Ceder, co-author of the paper and a professor at the university. 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.
“While the robotics of the A-lab is cool, the real innovation is the integration of various sources of knowledge and data with A-lab in order to intelligently drive synthesis,” he said.
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.
“This expansive database of inorganic crystals ought to be filled with ‘gems’ to be uncovered, to advance solutions to clean energy and environmental challenges,” said Yildiz, who works in MIT’s departments of materials science and engineering, and nuclear science and engineering.
The papers represented a further “very exciting advance” in the quest to “obtain materials at speeds far surpassing traditional empirical synthesis approaches,” she added.
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