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Scaling deep learning for materials discovery

Author

Listed:
  • Amil Merchant

    (Google DeepMind)

  • Simon Batzner

    (Google DeepMind)

  • Samuel S. Schoenholz

    (Google DeepMind)

  • Muratahan Aykol

    (Google DeepMind)

  • Gowoon Cheon

    (Google Research)

  • Ekin Dogus Cubuk

    (Google DeepMind)

Abstract

Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing1–11. 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 computation12–14. 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. Building on 48,000 stable crystals identified in continuing studies15–17, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Our work represents an order-of-magnitude expansion in stable materials known to humanity. 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. Of the stable structures, 736 have already been independently experimentally realized. The scale and diversity of hundreds of millions of first-principles calculations also unlock modelling capabilities for downstream applications, leading in particular to highly accurate and robust learned interatomic potentials that can be used in condensed-phase molecular-dynamics simulations and high-fidelity zero-shot prediction of ionic conductivity.

Suggested Citation

  • Amil Merchant & Simon Batzner & Samuel S. Schoenholz & Muratahan Aykol & Gowoon Cheon & Ekin Dogus Cubuk, 2023. "Scaling deep learning for materials discovery," Nature, Nature, vol. 624(7990), pages 80-85, December.
  • Handle: RePEc:nat:nature:v:624:y:2023:i:7990:d:10.1038_s41586-023-06735-9
    DOI: 10.1038/s41586-023-06735-9
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    Citations

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    Cited by:

    1. Chang Jiang & Hongyuan He & Hongquan Guo & Xiaoxin Zhang & Qingyang Han & Yanhong Weng & Xianzhu Fu & Yinlong Zhu & Ning Yan & Xin Tu & Yifei Sun, 2024. "Transfer learning guided discovery of efficient perovskite oxide for alkaline water oxidation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Gaétan de Rassenfosse & Adam B. Jaffe & Joel Waldfogel, 2024. "Intellectual Property and Creative Machines," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, National Bureau of Economic Research, Inc.
    3. David Buterez & Jon Paul Janet & Steven J. Kiddle & Dino Oglic & Pietro Lió, 2024. "Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    4. Ziduo Yang & Yi-Ming Zhao & Xian Wang & Xiaoqing Liu & Xiuying Zhang & Yifan Li & Qiujie Lv & Calvin Yu-Chian Chen & Lei Shen, 2024. "Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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