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Using a single complex to predict the reaction energy profile: a case study of Pd/Ni-catalyzed ethylene polymerization

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Machine learning for design principles for single atom catalysts towards electrochemical reactions

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Recent advances in solvent effects for biomass-derived platform molecule conversion: experimental and theoretical perspectives

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A 3d convolutional neural network autoencoder for predicting solvent configuration changes in condensed phase biomass reactions

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An automated reaction route mapping for the reaction of NO and active species on Ag4 clusters in zeolites

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Mechanistic insights and rational catalyst design in NOx electroreduction

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Designing main-group catalysts for low-temperature methane combustion by ozone

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Machine-Learning-Guided Discovery and Statistical Analysis of Multielemental Reverse Water–Gas Shift Catalysts

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Dielectric Saturation in Water from a Long-Range Machine Learning Model

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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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Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations

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Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H2 Activation Using Machine Learning-Accelerated First-Principles Simulations

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Machine learning-enhanced optimal catalyst selection for water-gas shift reaction

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Digital Chemical Engineering, 2024, 12, 100165
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Transition Metal‐Based High‐Entropy Materials for Catalysis

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Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning

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A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis

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Active learning-based automated construction of Hamiltonian for structural phase transitions: a case study on BaTiO3

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Atomistic Insights into the Oxidation of Flat and Stepped Platinum Surfaces Using Large-Scale Machine Learning Potential-Based Grand-Canonical Monte Carlo

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Towards physics-informed explainable machine learning and causal models for materials research

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Predicting hydrogenolysis reaction barriers of large hydrocarbons on metal surfaces using machine learning: Implications for polymer deconstruction

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Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

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Ethane dehydrogenation over the g-C3N4 supported metal single-atom catalysts to enhance reactivity and coking-resistance ability

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Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

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Evaluating the thermal stability of chemicals and systems: A review

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Machine learning methods for theoretical heterogeneous catalysis: current status and challenges

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Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands

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Model driven adaptive design with concentration profiles

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First Principles Identification of Active Sites in Heterogeneous Catalysis: A Case Study on ZnxCryOz for Syngas Conversion

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Inspirational perspectives and principles on the use of catalysts to create sustainability

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Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery

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Dynamic Metal–Support Interaction Dictates Cu Nanoparticle Sintering on Al2O3 Surfaces

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Indirect Measurement of Variables in a Heterogeneous Reaction for Biodiesel Production

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Accelerating the structure search of catalysts with machine learning

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Density Matrix-Based Features as Descriptors for Oxygen Reduction and Evolution Catalysts

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CatS: A predictive and user-friendly framework based on machine learning models for the screening of heterogeneous catalysts

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Accurate Simulations of the Reaction of H2on a Curved Pt Crystal through Machine Learning

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Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

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Bilayer MN4-O-MN4 by bridge-bonded oxygen ligands: Machine learning to accelerate the design of bifunctional electrocatalysts

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Design and Application of Electrocatalyst Based on Machine Learning

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Machine-learning atomic simulation for heterogeneous catalysis

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Recent progress towards a universal machine learning model for reaction energetics in heterogeneous catalysis

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First Principles Identification of Active Sites in Heterogeneous Catalysis: A Case Study on ZnxCryOz for Syngas Conversion

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Deep reaction network exploration at a heterogeneous catalytic interface

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Application of topology-based structure features for machine learning in materials science

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Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

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Catalytic reactivity descriptors of metal‐nitrogen‐doped carbon catalysts for electrocatalysis

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Quo vadis multiscale modeling in reaction engineering? – A perspective

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Surface restructuring and predictive design of heterogeneous catalysts

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Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis

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Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis

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Grand challenges on accelerating discovery in catalysis

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Efficient and universal characterization of atomic structures through a topological graph order parameter

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Achieving Theory–Experiment Parity for Activity and Selectivity in Heterogeneous Catalysis Using Microkinetic Modeling

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Machine learning for CO2 capture and conversion: A review

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Machine Learning of Reactive Potentials

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Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

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Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane

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