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RESEARCH PAPER · Advanced Science · 2023

Machine Learning Descriptors for Data‐Driven Catalysis Study

Li-Hui Mou, T.-T. Han, Pieter E. S. Smith, Edward H. Sharman, Jun Jiang

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Machine Learning Descriptors for Data‐Driven Catalysis StudyOriginalActivity and Selectivity Roadmap for C–N Electro-Coupling on MXenes115Advanced 3D ordered electrodes for PEMFC applications: From structural features and fabrication methods to the controllable design of catalyst layers47A systematic theoretical study of CO 2 hydrogenation towards methanol on Cu-based bimetallic catalysts: role of the CHO&CH 3 OH descriptor in thermodynamic analysis6Automation and machine learning augmented by large language models in a catalysis study107Computational chemistry for water-splitting electrocatalysis239Electrochemical CO 2 reduction on Pd-based electrodes: from mechanism understanding to rational catalyst design27Progress and challenges in nitrous oxide decomposition and valorization129Single-atom and cluster catalysts for thermocatalytic ammonia synthesis at mild conditions48Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning27Design Principle of Carbon-Supported Single-Atom Catalysts – Interplay between d-Orbital Periodicity and Local Hybridization15Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age8Machine Learning-Accelerated Disentanglement of Activity–Selectivity Trade-off of Multielement Oxide Denitration Catalysts13High throughput screening for electrocatalysts for nitrogen reduction reaction using metal-doped bilayer borophene: A combined approach of DFT and machine learning17Hidden descriptors: Using statistical treatments to generate better descriptor sets3Machine Learning to Promote Efficient Screening of Low‐Contact Electrode for 2D Semiconductor Transistor Under Limited Data18A Review on Fe 2 O 3 ‐Based Catalysts for Toluene Oxidation: Catalysts Design and Optimization with the Formation of Abundant Oxygen Vacancies34Toward Interpretable Machine Learning Models for Predicting Spectroscopy, Catalysis, and Reactions1Transition metal oxides in CO2 driven oxidative dehydrogenation: Uncovering their redox properties19Self‐Learning e‐Skin Respirometer for Pulmonary Disease Detection6CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces3Unveiling the energetic complexity of noncovalent interactions in halogenated dimers1Computational catalysis on the conversion of CO2 to methane—an update4From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190A Data‐Driven Approach for Enhanced CO 2 Capture with Ruthenium Complexes5Effective Screening Descriptors of Metal–Organic Framework-Supported Single-Atom Catalysts for Electrochemical CO 2 Reduction Reactions: A Computational Study48New mechanistic insights into CO2 electroreduction to methane on penta-octa-graphene catalyst6Predicting and understanding photocatalytic CO2 reduction reaction with IR spectroscopy-based interpretable machine learning framework15Synergistic N-heterocyclic carbene and C2N integration for efficient and selective metal-free photocatalytic CO reduction to C2H5OH3Catalysis in the digital age: Unlocking the power of data with machine learning75Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis48Data-Driven Discovery of Reaction Pathways for Modeling Catalytic Cracking of Hydrocarbons1Bifunctional Oxygen Reduction/Evolution Reaction Activity of Transition Metal-Doped T-C3N2 Monolayer: A Density Functional Theory Study Assisted by Machine Learning14Application of graph neural network in computational heterogeneous catalysis29Study and prediction of photocurrent density with external validation using machine learning models8Methanol Activation: Strategies for Utilization of Methanol as C1 Building Block in Sustainable Organic Synthesis14Fundamental understanding of fluorophenol-derived dual organocatalysts for ring-opening polymerization of lactide1Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning12Accelerating the global search of adsorbate molecule positions using machine-learning interatomic potentials with active learning6Advances in computational approaches for bridging theory and experiments in electrocatalyst design31Computational modelling of nanoparticle catalysis4Computational screening of M 2 N 4 -C-type dual-atom-catalysts for electrochemical ammonia synthesis by the first-principles DFT and machine learning3Efficient low-temperature thermochemical CO 2 splitting enabled by Gibbs free energy engineering4Literature-Based Machine Learning Framework for Yield Prediction in Cyclohexene Epoxidation: Toward a Digital Catalyst Concept0Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling1Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning10Transfer learning with domain knowledge adaptation for stability evaluation of dual-atom catalysts on nitrogen-doped carbon0Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis48AI-Driven Inverse Design of Materials: Past, Present, and Future78Harnessing the Cobalt-Catalyzed Hydrogen Evolution Reaction through a Data-Driven Approach7Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2Machine learning descriptor-assisted exploration of metal-modified graphene hydrogen storage materials10Physical Significance of Descriptors to Predict the Band Center of High‐Entropy Nanoalloys4Insights into Structure-Activity Relationships between Y Zeolites and their n-C10 Hydrocracking Performances via Machine Learning Approaches6Ferroelectric Polarization Effects of Single‐Atom Catalysts on Water Oxidation37Machine learning-based design of electrocatalysts and catalytic mechanism research4Guided electrocatalyst design through in-situ techniques and data mining approaches8Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives29From Single-Atom to Dual-Atom: A Universal Principle for the Rational Design of Heterogeneous Fenton-like Catalysts39PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals4Design and Application of Electrocatalyst Based on Machine Learning33A Paradigm Shift: From Batch Processing to Flow Chemistry4Multiscale Design of Array‐Type Integrated Electrodes for Gas‐Involving Electrocatalytic Reactions4Relevance of the electronic structure of the substrate to O 2 molecule adsorption on Fe-N-C single-atom catalysts under electrochemical potential4Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules18Mesoporous electrocatalysts for oxygen reduction reaction: From mechanisms, characterizations to designs16Descriptors toward higher alcohols and olefins formation in CO2 hydrogenation on promoted iron catalysts16Predicting Reactivity with Machine Learning1Lowering the barrier to access information-rich transient kinetic data for machine learning methods8Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion25Boosting Homogeneous Hydrogenation of CO2 via the Data‐Driven Approach2Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions via Experiments and AI5Structural Descriptor Bridging the Microstructural Feature and Catalytic Reactivity for Rational Design of Metal Catalysts14Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook10Recent Advances in Data‐Driven Design of Dual‐Atom Catalysts15Machine learning-driven insights into the microstructure and properties of high-entropy alloys14Transition metal sulfide electrocatalysts for water Splitting: Bridging computational design and experimental advancements28Application of an Explainable Machine Learning to CO2 Methanation for Optimal Design Nickel-Based Catalysts12Catalytic Oxidation of Ethanol for Treatment of Commercially Produced Carbon Dioxide Using Aqueous Sol–Gel-Derived Catalyst Materials0Data-driven design of Ni-based catalysts for CO2 methanation using interpretable machine learning4Styrene Polymerization with Group III Metallocenes: A Generative and Interpretable Catalyst Design Strategy Combining Density Functional Theory and Machine Learning3Recent development and challenges in TMD-based 2D materials towards OER/ORR electrocatalysis6Machine learning (ML)-assisted development of 2D green catalysts to support sustainability3Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques9Materials Discovery Using Uncertainty-Aware Constrained Bayesian Optimization With Representation Learning of High-Dimensional 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for hydrogen production: A comprehensive review0Familywise Feature Importance Stability in Chemical and Materials Machine Learning1Going beyond Conventional Catalytic Oxidation of Formaldehyde via Machine-Learning-Accelerated Design of Oxide–Silver Tandem Catalysts0Recent advances in metal-organic frameworks and their derivatives for the selective catalytic reduction of NOx0Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production1A Review on the Application of Machine Learning in Nitrogen Reduction Reaction0Explainable artificial intelligence shines a light on catalysis: methods, applications, and future directions0Decoding Interfacial Interactions Governing Organic Compounds Removal: Transfer of Feature Engineering from TiO 2 to Clay Minerals1Machine learning predictive models for optimization of reaction parameter over metal oxide promoters for syngas production via autothermal reforming0Layered double hydroxide-based 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multiscale feature-extraction framework for X-ray diffraction fingerprints0Engine exhaust waste heat recovery and utilization: synergistic optimization of Cu-based ternary catalysts for hydrogen-rich fuel production from green methanol cracking0Machine Learning-Driven Catalyst Screening, Mechanism Elucidation, and Process Optimization for CO2 Reduction0

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