Catalysis Research
(ISSN 2771-490X)
Catalysis Research is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. This periodical is devoted to publishing high-quality papers that describe the most significant and cutting-edge research in all areas of catalysts and catalyzed reactions. Its aim is to provide timely, authoritative introductions to current thinking, developments and research in carefully selected topics.
Topics contain but are not limited to:
- Photocatalysis
- Electrocatalysis
- Environmental catalysis
- Biocatalysis, enzymes, enzyme catalysis
- Catalysis for biomass conversion
- Organocatalysis, catalysis in organic and polymer chemistry
- Nanostructured catalysts
- Catalytic materials
- Computational catalysis
- Kinetics of catalytic reactions
The journal publishes a variety of article types: Original Research, Review, Communication, Opinion, Comment, Conference Report, Technical Note, Book Review, etc.
There is no restriction on paper length, provided that the text is concise and comprehensive. Authors should present their results in as much detail as possible, as reviewers are encouraged to emphasize scientific rigor and reproducibility.
Publication Speed (median values for papers published in 2024): Submission to First Decision: 4.8 weeks; Submission to Acceptance: 10.0 weeks; Acceptance to Publication: 8 days (1-2 days of FREE language polishing included)
Special Issue
Machine Learning in Catalysis: From Data to Discovery
Submission Deadline: September 30, 2025 (Open) Submit Now
Guest Editor
Leandro Goulart de Araujo, PhD
IRCELYON, Institut de Recherches sur la Catalyse et l’Environnement de Lyon, UMR5256 CNRS-Université de Lyon, 69626 Villeurbanne, France
Research Interests: Catalysis; advanced oxidation and adsorption processes; nuclear technology; data science and analytics
About This Topic
Catalysis is essential to a sustainable and efficient future, underpinning numerous processes and products across sectors. However, it remains a complex field, involving diverse feedstocks, catalysts, and operational conditions, as well as challenges related to cost, accessibility of materials, and limited datasets. While high-throughput experimentation offers speed, material consumption and computational costs remain limiting factors. Traditional modeling approaches, such as phenomenological or theory-based models, though powerful, often struggle with local minima, parameter uncertainty, and intensive resource requirements. Machine learning (ML) has emerged as a transformative tool to address these limitations. From enhancing data extraction using large language models to integrating ML with physics-based methods such as DFT, Monte Carlo, or CFD, the field is witnessing a paradigm shift. ML also enables uncertainty quantification when combined with statistical frameworks like Bayesian inference. This special issue welcomes contributions that explore the intersection of catalysis and ML, including (but not limited to): ML-driven data collection; hybrid approaches combining ML and mechanistic modeling; and ML applications for predictive modeling and uncertainty analysis.
Keywords
Machine learning; Catalysis; Data-driven modeling; Hybrid modeling; High-throughput data; Uncertainty quantification; Physics-informed ML; Large language models; DFT/ML integration; Bayesian methods
Manuscript Submission Information
Manuscripts should be submitted through the LIDSEN Submission System. Detailed information on manuscript preparation and submission is available in the Instructions for Authors. All submitted articles will be thoroughly refereed through a single-blind peer-review process and will be processed following the Editorial Process and Quality Control policy. Upon acceptance, the article will be immediately published in a regular issue of the journal and will be listed together on the special issue website, with a label that the article belongs to the Special Issue. LIDSEN distributes articles under the Creative Commons Attribution (CC BY 4.0) License in an open-access model. The authors own the copyright to the article, and the article can be free to access, distribute, and reuse provided that the original work is correctly cited.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). Research articles and review articles are highly invited. Authors are encouraged to send the tentative title and abstract of the planned paper to the Editorial Office (cr@lidsen.com) for record. If you have any questions, please do not hesitate to contact the Editorial Office.
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