AIMC Topic: Catalysis
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Applied Machine Learning for Prediction of Energy-Efficient CO Desorption on Solid Acid Catalysts.
Environmental science & technology
Aug 12, 2025
The development of solid acid catalysts (SACs) for energy-efficient CO desorption and amine regeneration is critical to carbon capture commercialization. To avoid the time-consuming and ineffective screening process, a predictive model correlating th...
Machine Learning-Assisted Discovery of Bimetallic Oxides for Highly Efficient Catalytic Ozonation.
Environmental science & technology
Aug 12, 2025
Catalytic ozonation stands out as an effective process in the advanced treatment of industrial wastewater, where heterogeneous catalysts play a pivotal role. Here, by screening 1603 bimetallic oxides via machine learning (ML), a pioneering ZnCuO was ...
Machine Learning-Driven Prediction of Electrochemical Promotion in the Reverse Water Gas Shift Reaction.
Journal of chemical information and modeling
Aug 11, 2025
Electrochemical promotion of catalysis (EPOC) provides an effective and versatile strategy to enhance catalytic activity, selectivity, and stability in the reverse water-gas shift (RWGS) reaction, facilitating efficient CO hydrogenation to syngas und...
Bioinspired rational design of nanozymes.
Materials horizons
Aug 11, 2025
Nanozymes, an emerging class of artificial enzymes, have attracted increasing attention for their potential in environmental monitoring, industrial catalysis, food safety, and biomedicine. To date, more than 1500 nanomaterials have been identified wi...
Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation.
Journal of chemical information and modeling
Jul 14, 2025
Catalytic epoxidations are key chemical processes serving as essential steps in the synthesis of commercially valuable compounds. This study presents an innovative supervised machine learning (ML) model to predict the reaction yield of the vanadium-c...
Catalytic mechanism and engineering of aromatic prenyltransferase: A review.
International journal of biological macromolecules
Jun 1, 2025
The prenylation of aromatic compounds significantly enhances their metabolic stability and bioactivity. Prenyltransferases, as essential biocatalysts, facilitate the regioselective transfer of prenyl groups from donors to aromatic substrates. This re...
Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation.
Journal of environmental management
Jun 1, 2025
In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of...
Enhancing photocatalytic degradation of hazardous pollutants with green-synthesized catalysts: A machine learning approach.
Journal of environmental management
Jun 1, 2025
The effective removal of organic pollutants from wastewater necessitates the development of advanced photocatalytic materials. This study explores the application of machine learning algorithms to predict the degradation efficiency of PRM using green...
Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO/NaOCl reaction pathways.
Scientific reports
May 29, 2025
This paper assesses the presentation of Gradient Boosting Regression (GBR), Ridge Regression (RR), and Particle Swarm Optimization (PSO) models in improving the photocatalytic destruction of antibiotic utilizing a UV/ZrO₂/NaOCl system. The GBR model ...
NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.
Briefings in bioinformatics
May 1, 2025
Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challengi...
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