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RESEARCH PAPER · Physical Chemistry Chemical Physics · 2020

High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery

Katherine McCullough, Travis Williams, Kathleen Mingle, Pooyan Jamshidi, Jochen A. Lauterbach

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High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discoveryOriginalAdvances in dynamically controlled catalytic reaction engineering31Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data35Catalyst design and tuning for oxidative dehydrogenation of propane – A review153[Retracted] Educational Information System Optimization for Artificial Intelligence Teaching Strategies41Toward autonomous design and synthesis of novel inorganic materials159Toward Machine Learning-Enhanced High-Throughput Experimentation155Automated Experimentation Powers Data Science in Chemistry111Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane54Machine learning for advanced energy materials208Titanium Complexes with Functional Alkoxido Ligands for Selective Ethylene Dimerization – A High Throughput Experimentation Approach6Segregation dynamics of a Pd-Ag surface during CO oxidation investigated by NAP-XPS20Operando Surface Spectroscopy and Microscopy during Catalytic Reactions: From Clusters via Nanoparticles to Meso‐Scale Aggregates94Process Systems Engineering Perspective on the Design of Materials and Molecules45Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem8A review on the recent developments of ruthenium and nickel catalysts for COx-free H2 generation by ammonia decomposition125Accelerating Optimizing the Design of Carbon‐based Electrocatalyst via Machine Learning21Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77Evaluation of a Data-Driven, Machine Learning Approach for Identifying Potential Candidates for Environmental Catalysts: From Database Development to Prediction17Mechanistic insights into selective oxidation and corrosion of multi-principal element alloys from high resolution and in situ microscopy10Perspective on integrating machine learning into computational chemistry and materials science203Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions28Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Self-Adaptable Tropos Catalysts22High‐Throughput Robotic Synthesis and Photoluminescence Characterization of Aqueous Multinary Copper–Silver Indium Chalcogenide Quantum Dots22Mechanisms, challenges, and opportunities of dual Ni/ photoredox‐catalyzed C(sp 2 )–C(sp 3 ) cross‐couplings37Application of machine learning algorithms for use in material chemistry12Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes162Automated synthesis and data accumulation for fast production of high-performance Ni nanocatalysts10Application of density functional theory and machine learning in heterogenous-based catalytic reactions for hydrogen production72Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning6PhasIR: An Instrumentation and Analysis Software for High-throughput Phase Transition Temperature Measurements5Knowledge extraction in catalysis utilizing design of experiments and machine learning29Defining inkjet printing conditions of superconducting cuprate films through machine learning14High-entropy alloy catalysts: high-throughput and machine learning-driven design59Machine learning for design principles for single atom catalysts towards electrochemical reactions116Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data12Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass76Autonomous high-throughput computations in catalysis29Structure–Activity Relationships in Lewis Acid–Base Heterogeneous Catalysis88Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter12Comparison of Catalysts with MIRA21 Model in Heterogeneous Catalytic Hydrogenation of Aromatic Nitro Compounds6Revising Message Passing Graph Neural Networks for Catalyst Design1HTE and Data Analysis for Discovery and Molecular-level Understanding of Catalysts0Catalytic oxidation of volatile organic compounds over manganese-based oxide catalysts: Performance, deactivation and future opportunities77Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation26Computational Catalyst Design for Dry Reforming of Methane: A Review40FINETUNA: fine-tuning accelerated molecular simulations36Automated defect detection for coatings via height profiles obtained by laser-scanning microscopy7Research Progress in High-Throughput Screening of CO2 Reduction Catalysts31Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects24Roadmap on chalcogenide photonics42Intelligent end-of-pipe catalytic systems suited for alternative fuels0Materials discovery of ion-selective membranes using artificial intelligence30Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts1A deep neural network for oxidative coupling of methane trained on high-throughput experimental data4Thermocatalytic Ammonia Decomposition – Status and Current Research Demands for a Carbon‐Free Hydrogen Fuel Technology39A modular low-cost automated synthesis machine demonstrated by ring-opening metathesis polymerization5Determination of enantiomeric excess and diastereomeric excess via optical methods. Application to α-methyl-β-hydroxy-carboxylic acids8Nanogel Catalysts for the Hydrolysis of Underivatized Disaccharides Identified by a Fast Screening Assay11High-Throughput Experimentation, Theoretical Modeling, and Human Intuition: Lessons Learned in Metal–Organic-Framework-Supported Catalyst Design25Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation48Rational design of high-entropy ceramics based on machine learning – A critical review52Machine learning-aided characterization of microbubbles for venturi bubble generator20Recent advances in ammonia synthesis over ruthenium single-atom-embedded catalysts: A focused review77Review on Ru-Based and Ni-Based Catalysts for Ammonia Decomposition: Research Status, Reaction Mechanism, and Perspectives154Developing Machine Learning Models for Catalysts in Oxidative Dehydrogenation of n‐butane8Research Status, Optimization Strategies, and Future Prospects of Ammonia Decomposition Catalysts for COx-Free Hydrogen57AI-Driven Copilot: Revolutionizing Scientific Discovery and Innovation through Self-Learning Systems1Biomass Gasification and Applied Intelligent Retrieval in Modeling22Catalyst‐Oriented Design Based on Elementary Reactions (CODER) for Triarylamine Synthesis9Catalyst‐Oriented Design Based on Elementary Reactions (CODER) for Triarylamine Synthesis0Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach61Machine Learning Accelerated Discovery of Subnanoparticles for Electrocatalytic Hydrogen Evolution1Prediction by a hybrid machine learning model for high-mobility amorphous In2O3: Sn films fabricated by RF plasma sputtering deposition using a nitrogen-mediated amorphization method9Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing22Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing1Fault Diagnosis of On-board Signaling Equipment Based on Bayesian Network0A review of mechanistic insights into CO2 reduction to higher alcohols for rational catalyst design39Data-driven Model Construction0Part I: Model Construction Theory0Overview of Applications of Artificial Intelligence (AI) in Diverse Fields9Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation56AI methods in materials design, discovery and manufacturing: A review142Recent progress of thermocatalytic ammonia synthesis via an associative mechanism15Machine learning for catalyst design: data matters3Recent advancements in noble-metal electrocatalysts for alkaline hydrogen evolution reaction82Tailoring the Olefin Selectivity in Catalytic Oxidative Dehydrogenation of Light Alkane by the Isolation Strategy37Combinatorial high throughput methodologies: the potentials in heterogeneous catalysts synthesis, screening and discovery—a review12Characterization of lithium phosphorus oxide thin film libraries by laser-induced breakdown spectroscopy imaging: A step towards high-throughput quantitative analyses9Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis11Embracing data science in catalysis research117A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis27Ammonia Energy: Synthesis and Utilization38Recent progress on ammonia cracking technologies for scalable hydrogen production66From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Toward accelerated discovery of solid catalysts using extrapolative machine learning approach10ROBERT : Bridging the Gap Between Machine Learning and Chemistry49Data-driven catalyst design for oxidative dehydrogenation of propane with CO2 using decision tree regression21Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation31Autonomous research and development of structural materials – An introduction and vision21Research status and advances of catalysts for hydrogen production from ammonia decomposition: Refined regulation methods24Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxides5Triggering nanoconfinement effect in advanced oxidation processes (AOPs) for boosted degradation of organic contaminants: A review56Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92Ensemble Model Approach for Predicting the Yield of Dehydrogenation Products during the Oxidative Dehydrogenation of n-Butane2Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning12Agglomeration-Resistant Ni Nanoparticles Generated by In Situ Hydrogenation Treatment of LaNiO3 as Catalyst for the Ammonia Decomposition11Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization22Flow chemistry as a tool for high throughput experimentation15High-Throughput Methods for Accelerated Catalyst Discovery3Investigation of arene and heteroarene nitration supported by high-throughput experimentation and machine learning0Knowledge - driven design of boron-based catalysts for oxidative dehydrogenation of propane2The structure–property relationship of metallocene-based ethylene oligomerization catalysts using DFT and graph neural networks4Accelerated discovery of nanostructured high-entropy alloys and multicomponent alloys via high-throughput strategies41An active representation learning method for reaction yield prediction with small-scale data8Artificial Intelligence‐Assisted Experimental Optimization of Water Oxidation Catalysts0Advanced TiO2-Based Photocatalytic Systems for Water Splitting: Comprehensive Review from Fundamentals to Manufacturing53The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design28Ethylene epoxidation over shape-selective silver-based catalysts2Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies34AI-driven high-throughput droplet screening of cell-free gene expression47Effects of Changing Atomic Concentration of a Single Element on the Yield Strength of High-Entropy Alloys: A Causal Inference Study0Data‐Driven Review on Aerogels and Polymeric Aerogels2Data‐Driven Review on Aerogels and Polymeric Aerogels45Insights into Structure-Activity Relationships between Y Zeolites and their n-C10 Hydrocracking Performances via Machine Learning Approaches6Peripheral effect promotes single-atom catalysis32Effect of Precursors on Trimetallic Ruthenium-Based Catalysts Supported on γ-Al2O3 Pellets for Low-Temperature Ammonia Decomposition8Artificial Intelligence‐assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data1Advancing chemical engineering technology with artificial intelligence21Machine learning for catalyst optimization: Outlier detection and material innovation6Flow-driven data intensification to accelerate autonomous inorganic materials discovery25High entropy alloys as catalysts: A focused review6Developing machine learning for heterogeneous catalysis with experimental and computational data93The role of artificial intelligence in optimizing photocatalytic degradation technologies of dyes in textile wastewater: Recent advances, challenges, and prospects13Machine learning application in thermal CO2 hydrogenation: catalyst design, process optimization, and mechanism insights13Advancing Organic Chemistry Using High‐Throughput Experimentation26Advancing Organic Chemistry Using High‐Throughput Experimentation7Artificial intelligence in chemical engineering and applied sciences education0Artificial intelligence in chemical engineering and process technologies0Data-driven design of polar organic semiconductors: A terminal group engineering approach5Autonomous catalysis research with human–AI–robot collaboration37From Macrokinetics to Microkinetics: A Perspective on Integrated and Rational Kinetic Comparison of Different Metal Catalysts for Carbonyl Hydrogenation1FastCat: Autonomous Discovery of Multielement Layered Double Hydroxide Alloy Catalysts for Alkaline Oxygen Evolution Reaction3Synthesis of optically active target molecules0Accelerating catalytic advancements through the precision of high-throughput experiments & calculations4Artificial Intelligence, Machine Learning, and High-Throughput Material Discovery Tools0Atomically tailored non-noble metal-based catalysts toward efficient propane dehydrogenation5Combinatorial to autonomous experimentation0

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