Artificial Intelligence- and Machine Learning-Driven Strategies for Catalyst Design and Sustainable Chemical Processes
- 1Department of Physical Chemistry and Electrochemistry, Faculty of Technology, University of Tuzla, Urfeta Vejzagića 8, 75000 Tuzla, Bosnia and Herzegovina
- 2Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia
Abstract
The integration of artificial intelligence (AI), machine learning (ML), and computational modeling with experimental catalysis is reshaping materials design and chemical process development. Tailored heterogeneous catalysts including supported metals, zeolites, defect-engineered materials, and multi-element systems exhibit enhanced activity, selectivity, and stability through engineered active sites and porosity. AI and ML approaches enable predictive modeling, high-throughput screening, mechanistic insight, and rational catalyst design by linking synthesis conditions, structural features, and performance metrics across scales. Applications span CO2 conversion, methane reforming, hydrogen production, polymer recycling, and photocatalysis, with platforms such as PHOTOREAC, QMOF, and PhotoCatDB facilitating the translation from laboratory experiments to reactor-scale processes. Hybrid strategies that combine mechanistic understanding with data-driven models improve interpretability, predictive accuracy, and process optimization. These advances underscore a paradigm shift toward data-driven catalysis, accelerating discovery, supporting sustainable chemical technologies, and emphasizing the role of human expertise in guiding responsible AI deployment.
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BibTeX
@article{bratovcic2026artificial,
title = {{Artificial Intelligence- and Machine Learning-Driven Strategies for Catalyst Design and Sustainable Chemical Processes}},
author = {Amra Bratovčić and Vesna Tomašić},
journal = {Processes},
year = {2026},
month = jun,
doi = {10.3390/pr14121866},
url = {https://doi.org/10.3390/pr14121866},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence- and Machine Learning-Driven Strategies for Catalyst Design and Sustainable Chemical Processes
AU - Amra Bratovčić
AU - Vesna Tomašić
PY - 2026
DA - 2026/06/09
JO - Processes
DO - 10.3390/pr14121866
UR - https://doi.org/10.3390/pr14121866
AB - The integration of artificial intelligence (AI), machine learning (ML), and computational modeling with experimental catalysis is reshaping materials design and chemical process development. Tailored heterogeneous catalysts including supported metals, zeolites, defect-engineered materials, and multi-element systems exhibit enhanced activity, selectivity, and stability through engineered active sites and porosity. AI and ML approaches enable predictive modeling, high-throughput screening, mechanistic insight, and rational catalyst design by linking synthesis conditions, structural features, and performance metrics across scales. Applications span CO2 conversion, methane reforming, hydrogen production, polymer recycling, and photocatalysis, with platforms such as PHOTOREAC, QMOF, and PhotoCatDB facilitating the translation from laboratory experiments to reactor-scale processes. Hybrid strategies that combine mechanistic understanding with data-driven models improve interpretability, predictive accuracy, and process optimization. These advances underscore a paradigm shift toward data-driven catalysis, accelerating discovery, supporting sustainable chemical technologies, and emphasizing the role of human expertise in guiding responsible AI deployment.
SN - 2227-9717
ER -