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Indirect design of OCM catalysts through machine learning of catalyst surface oxygen species

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Catal. Sci. Technol., 2023, 13, 5576
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Navigating epoxidation complexity: building a data science toolbox to design vanadium catalysts

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Synthesis and catalytic application of nanostructured metal oxides and phosphates

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Optimization of nickel-based catalysts for hydrogenolysis of light paraffins using machine learning

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Covalent sulfur–oxygen linkages enhanced the localized acidic environment coupled with the hydrogen spillover effect for boosting hydrogen evolution reaction kinetics

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Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane

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Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space

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https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

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Unveiling the relation between multiple chemical products and process conditions for trichloroethylene and perchloroethylene production via catalysis network analysis

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Bayesian Optimization-Guided Design of Silica-Supported Nickel Catalysts from Nickel Phyllosilicates

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ACS Eng. Au, 2025, 5, 425
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Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use

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Autonomous catalysis research with human–AI–robot collaboration

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Nat Catal, 2025, 8, 1135
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Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane

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Guided electrocatalyst design through in-situ techniques and data mining approaches

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Methanol Activation: Strategies for Utilization of Methanol as C1 Building Block in Sustainable Organic Synthesis

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ChemSusChem, 2025, 18
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Catalysis in the digital age: Unlocking the power of data with machine learning

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WIREs Comput Mol Sci, 2024, 14
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A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis

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Numerical Study to Define Initial Thermal Integration Window for Methane Oxidative Coupling with Dehydroaromatization Reactors

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International Journal of Chemical Engineering, 2023, 2023, 1
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Machine learning application in thermal CO2 hydrogenation: catalyst design, process optimization, and mechanism insights

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Advanced Powder Materials, 2025, 4, 100333
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Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use

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Unveiling the Hidden Energy Profiles of the Oxygen Evolution Reaction via Machine Learning Analyses

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Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

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ACS Omega, 2023, 8, 30335
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Machine learning knowledge‐driven Pt‐based catalyst design library development for selective oxidation of polyalcohol

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AIChE Journal, 2025, 71
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Development of graphical user interface for design of experiments via Gaussian process regression and its case study

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Compatibility Diagnosis for the Design of Oxidative Coupling of Methane Catalysts

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AILD: An Interpretable AI Framework for Designing Logical Descriptors in Predicting CO Adsorption on Catalysts

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Hypershape Recognition: A General Framework for Moment-Based Molecular Similarity

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Predicting CO Adsorption on Multi-elemental Alloy through Machine Learning Analysis of Ternary Components

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J. Comput. Chem. Jpn., 2025, 24, 30
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15 Years of Progress on Transition Metal-Based Electrocatalysts for Microbial Electrochemical Hydrogen Production: From Nanoscale Design to Macroscale Application

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Recent advances in catalyst design, performance, and challenges of metal-heteroatom-co-doped biochar as peroxymonosulfate activator for environmental remediation

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From molecules to data: the emerging impact of chemoinformatics in chemistry

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A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts

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High-throughput screening of multimetallic catalysts for three-way catalysis

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Descriptor Identification and Synthesis of Bimetallic Catalysts for CO2 Methanation

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Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data

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From density functional theory to machine learning: Emerging paradigms in energy materials discovery

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A Review on Fe2O3‐Based Catalysts for Toluene Oxidation: Catalysts Design and Optimization with the Formation of Abundant Oxygen Vacancies

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Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts

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Toward accelerated discovery of solid catalysts using extrapolative machine learning approach

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MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products

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An auto-configurable and interpretable ensemble learning framework for optimal catalyst design of green methanol production via Bayesian optimization

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Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

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Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane

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Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

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ACS Sustainable Chem. Eng., 2024, 12, 4121
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Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements

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