AI and Machine Learning Applications in Catalysis

Catalysis plays important roles in chemical transformations, impacting industries ranging from pharmaceuticals and materials to energy production. The design and optimisation of catalysts have traditionally relied on experimental trial-and-error approaches, which can be time-consuming and costly. Mechanistic understandings typically relied on combined experimental validation and computational modelling. Recent advances in artificial intelligence (AI) and machine learning (ML) are revolutionising this field by enabling data-driven chemical properties prediction, catalyst design, reaction optimisation, and mechanistic understanding. With increasing compute power and more reliable quantum chemical data, AI can uncover hidden patterns, predict reaction outcomes, and accelerate dynamical simulations at high accuracy with reduced costs for catalytic systems.

This Research Topic aims to highlight how AI and machine learning can overcome longstanding bottlenecks in catalysis research. While computational chemistry has provided valuable insights into reaction mechanisms, AI/ML offers a complementary toolkit to rapidly screen materials, predict chemical properties essential for efficient catalytic behaviour, and accelerate dynamical modelling. Key challenges include developing models that generalize across diverse reaction types, integrating experimental and computational data to improve predictive accuracy, and ensuring that AI-driven discoveries translate into practical, scalable solutions. We encourage contributions that showcase applications of AI/ML in catalytic science, including mechanistic studies as well as cutting-edge algorithms, tools and workflows that empower chemists to leverage AI insights. We aim to foster collaboration between experimental and computational chemists, so as to advance catalysis research toward a future of data-driven design.

We welcome submissions that explore the intersection of AI/ML and catalysis, including (but not limited to):
• Predictive Modelling: ML algorithms for forecasting catalytic activity, selectivity, or degradation.
• Reaction Optimisation: AI-guided design of catalysts, and/or reaction conditions.
• Data-Driven Discovery: Strategies to overcome data scarcity, such as transfer learning, synthetic data generation, or collaborative data-sharing platforms.
• Hybrid Approaches: Integrating AI with experimental techniques (e.g., robotics, high-throughput screening) or theoretical methods (e.g., DFT, microkinetic modeling).
• Case Studies: Real-world applications in energy storage, carbon capture, pollutant degradation, or industrial chemical synthesis.

Manuscript types include Original Research, Reviews, Perspectives, and Mini Reviews. We particularly encourage interdisciplinary studies that emphasize reproducibility, experimental validation, and practical impact. Submissions should clearly articulate how AI/ML advances catalytic science beyond conventional approaches.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Keywords: Artificial Intelligence (AI) and Machine Learning (ML), Catalysis, Computational Chemistry, Reaction Prediction and Optimization, Data-Driven Chemistry

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.