Scaling deep learning for materials discovery

Abstract Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing 1–11 . Of the stable structures, 736 have already been independently experimentally realized.

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• 1 From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches.
• 2 Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation 12–14 .
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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