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Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms

Author

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  • Tian Lan

    (Salesforce A.I. Research)

  • Huan Wang

    (Salesforce A.I. Research)

  • Qi An

    (Iowa State University)

Abstract

Exploring catalytic reaction mechanisms is crucial for understanding chemical processes, optimizing reaction conditions, and developing more effective catalysts. We present a reaction-agnostic framework based on high-throughput deep reinforcement learning with first principles (HDRL-FP) that offers excellent generalizability for investigating catalytic reactions. HDRL-FP introduces a generalizable reinforcement learning representation of catalytic reactions constructed solely from atomic positions, which are subsequently mapped to first-principles-derived potential energy landscapes. By leveraging thousands of simultaneous simulations on a single GPU, HDRL-FP enables rapid convergence to the optimal reaction path at a low cost. Its effectiveness is demonstrated through the studies of hydrogen and nitrogen migration in Haber-Bosch ammonia synthesis on the Fe(111) surface. Our findings reveal that the Langmuir-Hinshelwood mechanism shares the same transition state as the Eley-Rideal mechanism for H migration to NH2, forming ammonia. Furthermore, the reaction path identified herein exhibits a lower energy barrier compared to that through nudged elastic band calculation.

Suggested Citation

  • Tian Lan & Huan Wang & Qi An, 2024. "Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50531-6
    DOI: 10.1038/s41467-024-50531-6
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