ArticleNature communications2024
Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Advancing Lithium-Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence-Driven Design Paradigms.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Nanogalvanic cell catalysts: bridging electrochemical and thermal catalysis.National science review · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Developing machine learning for heterogeneous catalysis with experimental and computational data.Nature reviews. Chemistry · 2025Review
- Ammonia Synthesis over Transition Metal Catalysts: Reaction Mechanisms, Rate-Determining Steps, and Challenges.International journal of molecular sciences · 2025Review
- Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms.Nature communications · 2024Article
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Authors and funding
3 authors.
Funding
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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 NH
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Registered trials
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