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

Chemistry

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

T. Lan, H. Wang, et al.

Discover HDRL-FP, a revolutionary framework leveraging deep reinforcement learning to decode catalytic reaction mechanisms at unprecedented speed. This groundbreaking research by Tian Lan, Huan Wang, and Qi An showcases insights into hydrogen and nitrogen migration during ammonia synthesis, uncovering a transition state that simplifies processes with lower energy barriers.

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Playback language: English
Abstract
This paper introduces HDRL-FP, a reaction-agnostic framework using high-throughput deep reinforcement learning with first principles to explore catalytic reaction mechanisms. HDRL-FP represents reactions using atomic positions mapped to first-principles potential energy landscapes, enabling thousands of simultaneous simulations on a single GPU for rapid convergence. Its effectiveness is demonstrated by studying hydrogen and nitrogen migration in Haber-Bosch ammonia synthesis on Fe(111), revealing a shared transition state for Langmuir-Hinshelwood and Eley-Rideal mechanisms for H migration to NH2, forming ammonia with a lower energy barrier than nudged elastic band calculations.
Publisher
Nature Communications
Published On
Jul 25, 2024
Authors
Tian Lan, Huan Wang, Qi An
Tags
HDRL-FP
deep reinforcement learning
catalytic reactions
Haber-Bosch
ammonia synthesis
transition state
energy barrier

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