Density Functional Theory (DFT) Calculations and Machine Learning in Catalysis
A special issue of Catalysts (ISSN 2073-4344). This special issue belongs to the section "Computational Catalysis".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 49
Special Issue Editor
Special Issue Information
Dear Colleagues,
Catalysis is central to addressing global challenges in energy, sustainability, and chemical production. Density functional theory (DFT) and machine learning (ML) have emerged as powerful paradigms for accelerating catalyst discovery and optimization. In recent years, the transformative role of computational and data-driven approaches in advancing catalytic science has become evident. DFT provides atomic-level insights into reaction mechanisms, electronic structures, and active sites, while ML leverages large datasets to identify patterns, predict catalytic properties, and guide experimental design. Together, these tools enable researchers to explore vast chemical spaces, optimize reaction conditions, and design novel catalysts with enhanced activity, selectivity, and stability.
This Special Issue seeks to showcase cutting-edge research at the intersection of DFT, ML, and catalysis, including advancements in computational methods, data-driven catalyst design, and the application of these techniques to heterogeneous, homogeneous, and enzymatic catalysis. By bringing together contributions from leading researchers, the Issue will provide a comprehensive overview of how DFT and ML are reshaping the field, offering new opportunities for innovation and addressing complex challenges in catalysis. Original papers that explore theoretical developments, practical applications, and the integration of computational and experimental approaches, and which foster collaboration and drive progress in this rapidly evolving area, are welcome.
If you would like to submit papers for publication in this Special Issue or have any questions, please contact the in-house Editor, Mr. Ives Liu (ives.liu@mdpi.com).
Dr. Wei Xia
Guest Editor
Manuscript Submission Information
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Keywords
- catalysis
- density functional theory (DFT)
- machine learning (ML)
- computational catalysis
- catalyst discovery and optimization
- data-driven design
- reaction mechanisms and electronic structures
- heterogeneous/homogeneous/enzymatic catalysis
- sustainable energy and chemical production
- experimental-computational integration
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