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RESEARCH PAPER · Wiley Interdisciplinary Reviews Computational Molecular Science · 2024

Catalysis in the digital age: Unlocking the power of data with machine learning

Bokinala Moses Abraham, Mullapudi V. Jyothirmai, Priyanka Sinha, Francesc Viñes, Jayant Kumar Singh, Francesc Illas

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Catalysis in the digital age: Unlocking the power of data with machine learningOriginalDefects in MOFs for Photocatalytic Water Reduction to Hydrogen Generation: From Fundamental Understanding to State‐of‐Art Materials24Machine learning for a sustainable energy future15A brief overview of deep generative models and how they can be used to discover new electrode materials8Recent Advances in Single‐Atom Catalyst for Solar Energy Conversion: A Comprehensive Review and Future Outlook99Machine learning-assisted design and prediction of materials for batteries based on alkali metals8Performance assessment of various graph neural network architectures for predicting yields in cross-coupling reactions2Machine Learning-Guided Prediction of Activation Energies for Catalyst Design in the Water Gas Shift Reaction7Active Learning‐Driven Discovery of Sub‐2 Nm High‐Entropy Nanocatalysts for Alkaline Water Splitting28Three decades of quantum science: how quantum chemistry transformed thermochemical database generation for benchmarking DFT and machine learning9Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space8Ag/Eggshell Nanocatalyst for Sustainable Ethylbenzene Oxidation: Synthesis, Characterization, and Performance2Interpretable machine learning-assisted development of catalytic science theory1Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction2Design and Application of Electrocatalyst Based on Machine Learning33A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy2AI Approaches to Homogeneous Catalysis with Transition Metal Complexes47Machine learning in sludge Treatment: Applications, mechanisms, and prospects11Machine Learning-Assisted Spray Pyrolysis for the Synthesis of Single-Atom Fe–N–C Porous Hollow Microspheres for Zinc–Air Batteries11Recent Progress in Resistive Switching Memory Devices Covering Metal Oxides, Polymers, Bioinspired Materials, and Halide Perovskites12A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes4Decoding the coordination environment engineering of non-noble metal-nitrogen-carbon: From microstructure to oxygen electrocatalytic performance22Boosting Homogeneous Hydrogenation of CO2 via the Data‐Driven Approach2Advances in Gasoline Hydrodesulfurization Catalysts: The Role of Structure–Activity Relationships and Machine Learning Approaches13Recent Advances in Data‐Driven Design of Dual‐Atom Catalysts15MXgap: A MXene Learning Tool for Bandgap Prediction19Two-dimensional photocatalysts for photoreduction of CO223Nuances in liquid organic hydrogen carriers24Machine Learning–Enhanced Fischer–Tropsch Synthesis: Optimizing Catalysts and Process Conditions for Efficient Fuel Production3DFT and hybrid classical–quantum machine learning integration for photocatalyst discovery and hydrogen production16Machine Learning Approaches for Tackling Atomic-Scale Surface-Driven Complexities in Heterogeneous Catalysis0Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44Multi-feature deep learning framework for predicting CO adsorption mechanisms at metal oxide interfaces: a transformer-based approach1Non-radical persulfate activation on carbons: Mechanistic triangulation and descriptor-driven design for selective, byproduct-sparing water oxidation10Accurate Prediction of Adsorption and Diffusion Energies of Single Metal Atoms Supported on MXenes from Machine Learning6Graph neural networks reshaping the paradigm of electrocatalyst design for green hydrogen production10Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices15Machine learning-based prediction for stability of hydrogen species on metal-doped CeO 2 (111) surfaces0Machine learning in next-generation AEM fuel cells: a systematic review7Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces and interfaces5Synergistic enhancement of hydrogen evolution on MoS 2 by sulfur vacancy and transition metal doping: a DFT and machine learning study5Molecular property prediction: Input types and information processing in machine learning models5Machine Learning‐Driven Innovations in Energy and Environmental Catalysis: A Minireview0From density functional theory to machine learning: Emerging paradigms in energy materials discovery8Metal-organic frameworks and their derivatives for green energy fuel: Hydrogen production2Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence12Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance2Machine Learning Toolkits and Frameworks for Materials Design7Few-Shot Ensemble Learning for Catalysis and Application to Trimetallics for Oxygen Reduction6Emerging Trends and Future Prospects of the Catalytic Conversion of Syngas to Higher Alcohols3Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites7Electrifying catalysis: Joule heating-driven advanced materials for volatile organic compound oxidation0Integrating machine learning in catalyst design for sustainable hydrogen from plastic waste4Concurrent chemical reaction and cluster restructuring in a frustrated fluxional catalytic system identified via simple kMC model0Cost-Effective Quantum-Mechanical Workflows for Molecular Machine Learning4Photo‐/Electrochemical Site‐Selective Radical α‐C(sp 3 )–H Functionalization of Amines: Four Green Strategies for Applications, Challenges, and Opportunities2Recent advances in designing UiO-66-based photocatalysts for CO2 reduction and related photocatalytic applications: Structural tuning and emerging role of machine learning10Learning From Atoms: Machine Learning Enabled Design of Durable PtCo Fuel Cell Catalysts0Artificial Intelligence- and Machine Learning-Driven Strategies for Catalyst Design and Sustainable Chemical Processes5From Chatbots to Chemists: Autonomous Agents for Heterogeneous Catalysis0Design and screening of high-performance catalytic materials based on the synergistic effects of multimodal energy carriers: A review0Recent advances in metal-organic frameworks and their derivatives for the selective catalytic reduction of NOx0Catalysis Shaping a Sustainable Energy and Environmental Future0Dynamic Phase Evolution of Fe‐Based Catalysts During CO 2 Hydrogenation: Implications for Data–Driven Design0From Data to Discovery: Crafting Data‐Driven Frameworks for Advancing OER Electrocatalysts0Artificial intelligence in nanotechnology: current applications and future perspectives0Unveiling the Uncharted Potential of 2D Materials in Li/Na–S Batteries: A Paradigm Shift From Graphene0Artificial Intelligence and Machine Learning for Oxygen Reduction Reaction: A Review on Catalyst Discovery and Durability Modeling0Data- and AI-Driven Catalysis Science: From Descriptors to Digital Catalysis for Next-Generation Catalysts0Feature Selection in Multivariate Linear Regression Models for Data-Efficient Reaction Optimization1Modeling of pressurized electrolysis and co-electrolysis using reduced order model, computational fluid dynamics and artificial neural networks0A Hybrid ACO–Ensemble Learning Framework for Predicting Student Forum Consumption Behaviour0Engine exhaust waste heat recovery and utilization: synergistic optimization of Cu-based ternary catalysts for hydrogen-rich fuel production from green methanol cracking0Integrating machine learning-aided catalyst discovery and process electrification for decarbonizing platform chemicals0

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