AI and Theory in Catalysis Research
A special issue of Catalysts (ISSN 2073-4344). This special issue belongs to the section "Catalytic Reaction Engineering".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 37
Special Issue Editors
Interests: electronic structure and calculation of materials’ physical properties; physics-based design of catalytic materials; data science and machine learning applications in the physical sciences; excited-state and carrier dynamics
Interests: machine learning for spectra prediction and recognition; electronic structures at surfaces/interfaces; density functional theory; computational catalysis
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) and theoretical frameworks is revolutionizing catalysis research, bridging experimental, computational, and data-driven paradigms. AI enhances catalyst discovery by predicting activity and stability through machine learning (ML) models trained on data, reducing reliance on time-consuming trial-and-error methods. Techniques like active learning and generative models optimize synthesis conditions and propose novel catalyst candidates, accelerating high-throughput screening. Theoretical advancements, such as interpretable AI frameworks, now elucidate fundamental catalytic mechanisms. For instance, AI-derived descriptors reveal hidden relationships between catalyst structure and performance, enabling rational design. Machine learning potentials (MLPs) and quantum-inspired models further simulate complex reaction dynamics at atomic scales, bridging the gap between quantum mechanics and macroscopic observations. This synergy of AI and theory promises to assist in the development of sustainable catalytic solutions for energy, environment, and materials science.
This Special Issue is devoted to the current progress and the perspective tendencies of scientific research in AI and theory in catalysis research, with a primary focus on new techniques and applications for advanced studies.
Prof. Dr. Zhenpeng Hu
Dr. Hao Ren
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence in catalysis
- machine-learning-driven catalyst design
- high-throughput screening
- interpretable AI for catalytic mechanisms
- generative models for novel catalysts
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