Nanoengineers at the University of California San Diego’s Jacobs School of Engineering have developed an AI algorithm that predicts the structure and dynamic properties of any material—whether existing or new—almost instantaneously. Known as M3GNet, the algorithm was used to develop matterverse.ai, a database of more than 31 million yet-to-be-synthesized materials with properties predicted by machine learning algorithms. Matterverse.ai facilitates the discovery of new technological materials with exceptional properties.
The post Nanoengineers Develop Predictive Database for Materials appeared first on HPCwire.
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