Predictive Modeling in Catalysis
A special issue of Catalysts (ISSN 2073-4344). This special issue belongs to the section "Computational Catalysis".
Deadline for manuscript submissions: 10 November 2025 | Viewed by 37
Special Issue Editor
Interests: theoretical chemistry; molecular engineering; computational catalysis; materials informatics; cheminformatics; machine learning
Special Issue Information
Dear Colleagues,
Accurate prediction of catalytic performance—including activity, selectivity, and stability—is the cornerstone of rational catalyst design. This Special Issue focuses on realistic and robust modeling strategies that bridge theory and experiment to enable predictive insights into both heterogeneous and molecule-based catalytic systems. We welcome contributions that employ a range of methodologies of different natures and scales, including quantum mechanical calculations, statistical mechanics, and data-driven approaches (both interpretable machine learning and deep learning), with a balance of mechanistic insights and designs that leverage current knowledge. Emphasis is placed on models that go beyond simplistic or idealized systems to capture complex catalytic environments and phenomena, offering predictive power across reaction conditions, time and size scales, and material classes. We especially encourage approaches that challenge the status quo, i.e., existing design principles, paradigms, and workflows. By highlighting the advances in predictive models, this collection aims to advance the state-of-the-art in computational catalysis, connect the worlds of experiments and theories, and accelerate the discovery and optimization of practical catalytic systems.
Dr. Zisheng Zhang
Guest Editor
Manuscript Submission Information
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Keywords
- catalysis
- computational catalysis
- realistic modeling
- multi-scale modeling
- surface reconstruction
- reaction kinetics
- machine learning
- surface science
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