![]() Of the stable structures, 736 have already been independently experimentally realized. Stable discoveries that are on the final convex hull will be made available to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. Our work represents an order-of-magnitude expansion in stable materials known to humanity. Building on 48,000 stable crystals identified in continuing studies, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. ![]() “Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. ![]() A technical paper titled “Scaling deep learning for materials discovery” was published by researchers at Google DeepMind and Google Research.
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