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RESEARCH PAPER · Journal of the American Chemical Society · 2024

Generative Pretrained Transformer for Heterogeneous Catalysts

Dong Hyeon Mok, Seoin Back

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Generative Pretrained Transformer for Heterogeneous CatalystsOriginalLeveraging Machine learning and active motifs-based catalyst design for discovery of oxygen reduction electrocatalysts for hydrogen peroxide production17Large Language Model-Driven Knowledge Discovery for Designing Advanced Micro/Nano Electrocatalyst Materials4Metallic PtC monolayer as a promising hydrogen evolution electrocatalyst1AI-Driven Inverse Design of Materials: Past, Present, and Future78BODIPY-based supramolecular complexes as oxygen reduction reaction and hydrogen evolution reaction bifunctional electrocatalysts for Zinc-air battery8Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models28Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models1Applications of natural language processing and large language models in materials discovery132Big data-driven machine learning transformation for atomic-scale heterogeneous catalyst design: a critical review11Using machine learning to guide the synthesis of supported palladium catalysts with desired palladium dispersion6Computational catalysis and machine learning applications to water treatment technologies4Application of generative artificial intelligence in catalysis7Modeling Nonradiative Recombination in CsPbI3 and Ge-Doped Perovskites Via Deep Learning with CNN and Transformer Architectures1Bio-Digital Catalyst Design: Generative Deep Learning for Multi-Objective Optimization and Chemical Insights in CO 2 Methanation14Transforming Catalysis with Machine Learning: Emerging Tools and Next-Gen Strategies5Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H 2 O 2 Production via Data Science19Theoretical advances of simulating the dynamic structures of metal nanocatalysts under reaction conditions0Deep Learning for Computational Heterogeneous Catalysis: Fundamentals and Applications3Large Language Models for Heterogeneous Catalysis10Recent Advances in CO (2) Electroreduction Driven by Artificial Intelligence and Machine Learning14Deep learning guided generation of uranyl peroxide clusters via fullerene inspired topologies1Generative Adversarial Network-Enhanced Heterogeneous Ensemble Learning for Interpretable Prediction of CO2-to-Methanol Catalyst Performance3Synthesis-Aware Materials Redesign via Large Language Models12Autonomous catalysis research with human–AI–robot collaboration37Large Language Models (LLMs) for Materials Design1430 years of AI for electrocatalysis: Where we are and what’s next?12Energy catalysis through high-entropy materials by experiment, computation, and artificial intelligence7Feature engineering methods for machine learning in heterogeneous catalysis4Roadmap for transforming heterogeneous catalysis with artificial intelligence27Artificial Intelligence and Machine Learning in Advanced Materials Science0Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence12Generative Active Learning Discovers High-Performance O2 Reduction Catalysts for H2O2 Production2Synthesis methods, network selection, catalytic mechanism, and stability challenges of silver, gold, palladium, nickel, and cobalt nanoparticles: A review0Machine Learning‐Based Inverse Design for Functional Materials: Methods, Challenges, and Engineering Applications19A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era6A survey of agentic materials science and engineering: where are we and where are we going?0From Chatbots to Chemists: Autonomous Agents for Heterogeneous Catalysis0Closing the loop with generative AI and automated experimentation in electrochemical energy innovation0Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis0Autonomous laboratories for sustainable nanomaterials discovery0Artificial intelligence-driven inverse design of catalytic materials0Toward Precise, Multiscale, and Process-Aware Catalysis Generation via Generative AI0Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models1Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis0New Frontiers in Single-Atom Catalysis: Active-Site Dynamics and AI-Enabled Design1Generative Chemical Language Models for Energetic Materials Discovery0ZeoAgent: Autonomous Design of Zeolite Frameworks through Pore-Topology-Guided Generation and Evaluation0Catalyst‐Specialized Chemical Language Model Based on Transformer Variational Autoencoder for Catalyst Design and Discovery0Machine Learning‐Driven Rational Design of Organic Electro‐Oxidation Catalysts: A Mechanism‐Embedded Approach to Selectivity Engineering0Reaction-anchored generative informatics for million-scale CO2 catalyst screening via multi-adsorbate constraints0Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering0

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  1. Leveraging Machine learning and active motifs-based catalyst design for discovery of oxygen reduction electrocatalysts for hydrogen peroxide production

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  2. Large Language Model-Driven Knowledge Discovery for Designing Advanced Micro/Nano Electrocatalyst Materials

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  3. Metallic PtC monolayer as a promising hydrogen evolution electrocatalyst

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  4. AI-Driven Inverse Design of Materials: Past, Present, and Future

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  5. BODIPY-based supramolecular complexes as oxygen reduction reaction and hydrogen evolution reaction bifunctional electrocatalysts for Zinc-air battery

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  6. Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

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  7. Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

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  8. Applications of natural language processing and large language models in materials discovery

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  9. Big data-driven machine learning transformation for atomic-scale heterogeneous catalyst design: a critical review

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  14. Bio-Digital Catalyst Design: Generative Deep Learning for Multi-Objective Optimization and Chemical Insights in CO 2 Methanation

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  15. Transforming Catalysis with Machine Learning: Emerging Tools and Next-Gen Strategies

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  16. Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H 2 O 2 Production via Data Science

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  17. Theoretical advances of simulating the dynamic structures of metal nanocatalysts under reaction conditions

    Scientia Sinica Chimica · 2025-08-06

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  18. Deep Learning for Computational Heterogeneous Catalysis: Fundamentals and Applications

    Journal of the Indian Institute of Science · 2025-08-25

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  19. Large Language Models for Heterogeneous Catalysis

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  20. Recent Advances in CO (2) Electroreduction Driven by Artificial Intelligence and Machine Learning

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  21. Deep learning guided generation of uranyl peroxide clusters via fullerene inspired topologies

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  22. Generative Adversarial Network-Enhanced Heterogeneous Ensemble Learning for Interpretable Prediction of CO2-to-Methanol Catalyst Performance

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  23. Synthesis-Aware Materials Redesign via Large Language Models

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  24. Autonomous catalysis research with human–AI–robot collaboration

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  25. Large Language Models (LLMs) for Materials Design

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  26. 30 years of AI for electrocatalysis: Where we are and what’s next?

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  28. Feature engineering methods for machine learning in heterogeneous catalysis

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  32. Generative Active Learning Discovers High-Performance O2 Reduction Catalysts for H2O2 Production

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