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RESEARCH PAPER · ACS Catalysis · 2019

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Takashi Toyao, Zen Maeno, Satoru Takakusagi, Takashi Kamachi, Ichigaku Takigawa, Ken‐ichi Shimizu

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Machine Learning for Catalysis Informatics: Recent Applications and ProspectsOriginalHigh-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials112Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130Recent Progress in the Development of Screening Methods to Identify Electrode Materials for the Oxygen Evolution Reaction69Established and Emerging Computational Tools to Study Homogeneous Catalysis—From Quantum Mechanics to Machine Learning70Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning114Single-atom site catalysts for environmental catalysis324Machine learning in experimental materials chemistry66Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning109Revealing property-performance relationships for efficient CO2 hydrogenation to higher hydrocarbons over Fe-based catalysts: Statistical analysis of literature data and its experimental validation96The Catalytic Mechanics of Dynamic Surfaces: Stimulating Methods for Promoting Catalytic Resonance128Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys100Design of Polymer Blend Electrolytes through a Machine Learning Approach67Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future185Catalyst design and tuning for oxidative dehydrogenation of propane – A review153Predictions of adsorption energies of methane-related species on Cu-based alloys through machine learning64Nanoarchitectonics Revolution and Evolution: From Small Science to Big Technology75Exploitation of Intrinsic Confinement Effects of MOFs in Catalysis76Accelerated identification of high-performance catalysts for low-temperature NH 3 -SCR by machine learning41Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules113Developing sustainable, high-performance perovskites in photocatalysis: design strategies and applications254The reverse water gas shift reaction: a process systems engineering perspective369Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane54Machine learning for advanced energy materials208Sustainable Batteries—Quo Vadis?80Machine learning for perovskite materials design and discovery536Understanding electrified interfaces59A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts79Transfer learning for solvation free energies: From quantum chemistry to experiments170Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts122Catalytic Methylation of m-Xylene, Toluene, and Benzene Using CO2 and H2 over TiO2-Supported Re and Zeolite Catalysts: Machine-Learning-Assisted Catalyst Optimization40A perspective on the electrocatalytic conversion of carbon dioxide to methanol with metallomacrocyclic catalysts54Open Catalyst 2020 (OC20) Dataset and Community Challenges765The Sabatier Principle in Electrocatalysis: Basics, Limitations, and Extensions600Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77Perspective on integrating machine learning into computational chemistry and materials science203InVO4-based photocatalysts for energy and environmental applications95Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Machine learning for alloys698Singlet Oxygen Photosensitization Using Graphene-Based Structures and Immobilized Dyes: A Review41Quantum Dot Photocatalysts for Organic Transformations102Emerging applications of zeolites in catalysis, separation and host–guest assembly585Machine-Learned Decision Trees for Predicting Gold Nanorod Sizes from Spectra42ZnIn2S4‐Based Photocatalysts for Energy and Environmental Applications319Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models44Molecular Dynamics and Machine Learning in Catalysts46Building a Library for Catalysts Research Using High‐Throughput Approaches45The role of machine learning to boost the bioenergy and biofuels conversion170Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction110Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis97Knowledge-Embedded Message-Passing Neural Networks: Improving Molecular Property Prediction with Human Knowledge45Emerging Electrocatalysts for Water Oxidation under Near‐Neutral CO2 Reduction Conditions75Dynamics of Heterogeneous Catalytic Processes at Operando Conditions87Computational modeling of green hydrogen generation from photocatalytic H2S splitting: Overview and perspectives40Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO 2 Hydrogenation to Methanol117Application of density functional theory and machine learning in heterogenous-based catalytic reactions for hydrogen production72Accelerated dinuclear palladium catalyst identification through unsupervised machine learning164Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis53A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (S N Ar) using simple descriptors43A reactivity model for oxidative addition to palladium enables quantitative predictions for catalytic cross-coupling reactions63Interfacing single-atom catalysis with continuous-flow organic electrosynthesis123Machine learning for design principles for single atom catalysts towards electrochemical reactions116Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Automatic strain sensor design via active learning and data augmentation for soft machines101Recent advances on the nanoporous catalysts for the generation of renewable fuels56Roadmap on Machine learning in electronic structure180Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction91Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges118Interpretable machine learning for knowledge generation in heterogeneous catalysis428Materials Nanoarchitectonics from Atom to Living Cell: A Method for Everything101Structure–Activity Relationships in Lewis Acid–Base Heterogeneous Catalysis88Machine Learning-Assisted Screening of Stepped Alloy Surfaces for C1 Catalysis40Experimental characterization techniques for plasmon-assisted chemistry127Kinetics-informed neural networks46Tandem catalysis: A sustainable alternative for direct hydrogenation of CO2 to light olefins66A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation182Experimental and Computational Synergistic Design of Cu and Fe Catalysts for the Reverse Water–Gas Shift: A Review148Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors294Atomic‐Level Design of Active Site on Two‐Dimensional MoS 2 toward Efficient Hydrogen Evolution: Experiment, Theory, and Artificial Intelligence Modelling99Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation45Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for the oxygen reduction reaction90Machine Learning: A New Paradigm in Computational Electrocatalysis113Extending machine learning beyond interatomic potentials for predicting molecular properties167Interfacial component coupling effects towards precise heterostructure design for efficient electrocatalytic water splitting158Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies48Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Unraveling the Synergistic Reaction and the Deactivation Mechanism for the Catalytic Degradation of Double Components of Sulfur-Containing VOCs over ZSM-5-Based Materials89Catalysis for an electrified chemical production61Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis83Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning53Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models49Advancement of modification engineering in lean methane combustion catalysts based on defect chemistry71Data-driven design of electrocatalysts: principle, progress, and perspective46Machine learning integrated photocatalysis: progress and challenges94Electron Microscopy Studies of Soft Nanomaterials107Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**64Machine‐Learning‐Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes161Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction41Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation48Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170The value of negative results in data-driven catalysis research72Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles689Investigation and optimization of biodiesel production based on multiple machine learning technologies54Machine Learning Descriptors for Data‐Driven Catalysis Study154Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors71A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry56Revisiting Electrocatalyst Design by a Knowledge Graph of Cu-Based Catalysts for CO2 Reduction45Chemistry of Materials Nanoarchitectonics for Two-Dimensional Films: Langmuir–Blodgett, Layer-by-Layer Assembly, and Newcomers74Machine Learning-Guided Development of Trialkylphosphine Ni (I) Dimers and Applications in Site-Selective Catalysis43Dye sensitization of TiO2 relevant to photocatalytic hydrogen generation: Current research trends and prospects115A deep learning framework to emulate density functional theory81Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach61Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement56Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis161Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane69Scope of machine learning in materials research—A review230Machine learning for expediting next-generation of fire-retardant polymer composites43Automation and machine learning augmented by large language models in a catalysis study107Machine learning in nanozymes: from design to application51Progress and challenges in nitrous oxide decomposition and valorization129Revolutionizing ORR catalyst design through computational methodologies and materials informatics43Single-atom and cluster catalysts for thermocatalytic ammonia synthesis at mild conditions48Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60Lead-Free Double Perovskites: A Review of the Structural, Optoelectronic, Mechanical, and Thermoelectric Properties Derived from First-Principles Calculations, and Materials Design Applicable for Pedagogical Purposes94Advances and challenges in high-performance cathodes for protonic solid oxide fuel cells and machine learning-guided perspectives57Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data49Advances in in situ/operando techniques for catalysis research: enhancing insights and discoveries97Machine learning for CO 2 capture and conversion: A review60Enhancing biomass conversion to bioenergy with machine learning: Gains and problems41Embracing data science in catalysis research117Nanocrystal Assemblies: Current Advances and Open Problems124Active learning streamlines development of high performance catalysts for higher alcohol synthesis75From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Top 20 influential AI-based technologies in chemistry50Activity versus stability of atomically dispersed transition-metal electrocatalysts163Self-Driving Laboratories for Chemistry and Materials Science674Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Catalysis in the digital age: Unlocking the power of data with machine learning75Machine learning-enhanced photocatalysis for environmental sustainability: Integration and applications53Data Generation for Machine Learning Interatomic Potentials and Beyond176Optimizing CO 2 electroreduction: theoretical insights for enhancing efficiency across elementary steps65Recent advances in improving utilization efficiency of precious metal catalysts for hydrogen generation from hydrolysis of ammonia borane43Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis48A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes41Applications of density functional theory and machine learning in nanomaterials: A review51AI Approaches to Homogeneous Catalysis with Transition Metal Complexes47Artificial Intelligence for Materials Discovery, Development, and Optimization171Scalable Lignin Monomer Production Via Machine Learning‐Guided Reductive Catalytic Fractionation of Lignocellulose59Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44

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