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

Tian Lan, Huan Wang and Qi An ()
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Tian Lan: Salesforce A.I. Research
Huan Wang: Salesforce A.I. Research
Qi An: Iowa State University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-50531-6

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