Scaling deep learning for materials discovery

🤖 Plain-English Summary

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.

🔑 Key Findings

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

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