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RESEARCH PAPER · Chemistry of Materials · 2019

Enabling Catalyst Discovery through Machine Learning and High-Throughput Experimentation

Travis Williams, Katherine McCullough, Jochen A. Lauterbach

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Enabling Catalyst Discovery through Machine Learning and High-Throughput ExperimentationOriginalAdvances in dynamically controlled catalytic reaction engineering31High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Automated in Silico Design of Homogeneous Catalysts186Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning30Material Discovery and High Throughput Exploration of Ru Based Catalysts for Low Temperature Ammonia Decomposition75Ultra-High-Throughput Acoustic Droplet Ejection-Open Port Interface-Mass Spectrometry for Parallel Medicinal Chemistry84Machine learning in experimental materials chemistry66Towards Experimental Handbooks in Catalysis62Entre la comprensión y la predicción del funcionamiento de catalizadores sólidos0Catalyst design and tuning for oxidative dehydrogenation of propane – A review153Machine learning application to predict yields of solid products from biomass torrefaction96Applying machine learning to boost the development of high-performance membrane electrode assembly for proton exchange membrane fuel cells75Operando Surface Spectroscopy and Microscopy during Catalytic Reactions: From Clusters via Nanoparticles to Meso‐Scale Aggregates94High Throughput Centrifugal Adhesion Screening Test for Soft Materials0Process Systems Engineering Perspective on the Design of Materials and Molecules45Two-step machine learning enables optimized nanoparticle synthesis267Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage23Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77High-Throughput Screening Test for Adhesion in Soft Materials Using Centrifugation13Accelerating the Design of Automotive Catalyst Products Using Machine Learning3Technological Innovations in Photochemistry for Organic Synthesis: Flow Chemistry, High-Throughput Experimentation, Scale-up, and Photoelectrochemistry781Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage260Mechanisms, challenges, and opportunities of dual Ni/ photoredox‐catalyzed C(sp 2 )–C(sp 3 ) cross‐couplings37Quantum Mechanics Enables "Freedom of Design" in Molecular Property Space1Learning the Use of Artificial Intelligence in Heterogeneous Catalysis9Artificial intelligence: A powerful paradigm for scientific research1702Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes162Knowledge extraction in catalysis utilizing design of experiments and machine learning29Machine learning for design principles for single atom catalysts towards electrochemical reactions116Progress, challenges and perspectives of computational studies on glassy superionic conductors for solid-state batteries31Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Tracking Janus microswimmers in 3D with machine learning12A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data12Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence54Recent advances on the nanoporous catalysts for the generation of renewable fuels56Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass76Challenges and Opportunities for Renewable Ammonia Production via Plasmon‐Assisted Photocatalysis62Machine learning models for estimating above ground biomass of fast growing trees45Comparison of Catalysts with MIRA21 Model in Heterogeneous Catalytic Hydrogenation of Aromatic Nitro Compounds6Revising Message Passing Graph Neural Networks for Catalyst Design1Transfer learning using attentions across atomic systems with graph neural networks (TAAG)34Theory‐Guided Machine Learning to Predict the Performance of Noble Metal Catalysts in the Water‐Gas Shift Reaction11Electrocatalytic Oxygen Reduction to Produce Hydrogen Peroxide: Rational Design from Single-Atom Catalysts to Devices83FINETUNA: fine-tuning accelerated molecular simulations36Machine learning accelerated DFT research on platinum-modified amorphous alloy surface catalysts20Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective6Implementation of High Throughput Experimentation across Medicinal Chemistry, Process Chemistry and Materials Science5Automated and intelligent chemical synthesis5Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning53“Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties16A comprehensive machine learning strategy for designing high-performance photoanode catalysts21Data-driven design of electrocatalysts: principle, progress, and perspective46Machine learning facilitating the rational design of nanozymes49Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm15Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning5Computational Modeling of 4d and 5d Transition Metal Catalysts0Bridging the complexity gap in computational heterogeneous catalysis with machine learning280The value of negative results in data-driven catalysis research72Recent advances in ammonia synthesis over ruthenium single-atom-embedded catalysts: A focused review77Machine Learning Descriptors for Data‐Driven Catalysis Study154Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6Green ammonia to Hydrogen: Reduction and oxidation catalytic processes55Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning29Advancing high-throughput screening systems for synthetic biology and biofoundry31Scope of machine learning in materials research—A review230Interpretable Machine Learning with Physics-Based Feature Engineering: Application to the Catalytic Cracking Reaction of Polycyclic Aromatic Hydrocarbons8Accelerating active catalyst discovery: a probabilistic prediction-based screening methodology with applications in dry reforming of methane4Parallel Catalyst Synthesis Protocol for Accelerating Heterogeneous Olefin Polymerization Research7Artificial intelligence in catalysis2Catalytic Decomposition of Tritiated Ammonia as a By-Product of Magnetically Confined Nuclear Fusion0Experimental discovery of novel ammonia synthesis catalysts via active learning21Shallow Learning vs. Deep Learning in Engineering Applications13Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation56Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning66Combinatorial high throughput methodologies: the potentials in heterogeneous catalysts synthesis, screening and discovery—a review12Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation11Machine learning for predicting catalytic ammonia decomposition: An approach for catalyst design and performance prediction32Introduction of machine learning and artificial intelligence in biofuel technology50Interfacing High-Throughput Electrosynthesis and Mass Spectrometric Analysis of Azines10Catalyst Discovery for Propane Dehydrogenation through Interpretable Machine Learning: Leveraging Laboratory-Scale Database and Atomic Properties17From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Catalytic decomposition of NH3 as a by-product of magnetically confined nuclear fusion5Catalysis in the digital age: Unlocking the power of data with machine learning75ROBERT : Bridging the Gap Between Machine Learning and Chemistry49Active learning assisted piezoelectric materials synthesis on the basis of composite decision-making10High‐Throughput Screening Technologies of Efficient Catalysts for the Ammonia Economy6Artificial intelligence and machine learning at various stages and scales of process systems engineering36Application of Artificial Intelligence and Image Processing for the Cultivation of Chlorella sp. Using Tubular Photobioreactors51Phase transition mechanism and property prediction of hafnium oxide-based antiferroelectric materials revealed by artificial intelligence3AI Tools for Teaching-Learning Chemistry1Unifying thermochemistry concepts in computational heterogeneous catalysis13Challenges and future scope1Covalent organic and metal organic frameworks based single atom catalysts for valorisation of CO 2 to value added chemicals37Knowledge - driven design of boron-based catalysts for oxidative dehydrogenation of propane2Machine learning workflows beyond linear models in low-data regimes18Physics-aware feature learning for integrated energy systems management0Recent progress in the decomposition of ammonia as a potential hydrogen-carrier using green technologies34The structure–property relationship of metallocene-based ethylene oligomerization catalysts using DFT and graph neural networks4Torrefaction0Tailored group contribution methods designed using machine learning to predict enthalpies in carboxylic acids and anhydrides3Using Classifiers To Predict Catalyst Design for Polyketone Microstructure18AI-Driven Inverse Design of Materials: Past, Present, and Future78Enhancing Catalyst Performance Prediction with Hybrid Quantum Neural Networks: A Comparative Study on Data Consistency Variation4Artificial Intelligence‐Assisted Experimental Optimization of Water Oxidation Catalysts0Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics69Advanced TiO2-Based Photocatalytic Systems for Water Splitting: Comprehensive Review from Fundamentals to Manufacturing53Machine learning and experimental study on the activity decrease of VW/Ti for SCR at ultra-high temperature: The influence mechanism and regulation strategy7Machine Learning Optimized FeCoMn‐Trimetallic MOF‐Decorated Nanofibers for Enhanced OER Catalysis14Effect of Precursors on Trimetallic Ruthenium-Based Catalysts Supported on γ-Al2O3 Pellets for Low-Temperature Ammonia Decomposition8ML@ChemE: Past, Present, and Future of Machine Learning in Chemical Engineering11Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules18Machine Learning Driven Synthesis of Cobalt Oxide Entrapped Heteroatom-Doped Graphitic Carbon Nitride for Enhanced Oxygen Evolution Reaction2Processes for fabricating carbon-based supports in Ru-based catalysts in ammonia decomposition: A state-of-art review4Artificial Intelligence‐assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data1Advancing chemical engineering technology with artificial intelligence21Advancing Organic Chemistry Using High‐Throughput Experimentation26Advancing Organic Chemistry Using High‐Throughput Experimentation7AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31Unveiling the Power of Proximity of Prevalent Fe-Based Tandem Catalysts in CO 2 Hydrogenation via Modified Fischer–Tropsch: Crucial Relations toward Industrialization22Thermocatalytic ammonia synthesis beyond conventional Haber-Bosch: Principles, advances, challenges and opportunities18Artificial intelligence-driven nanomaterial design and applications: advances, challenges, and future directions0Molecularly engineered zeolites with extra-large-pore architectures and functional opportunities5Electrode–microbe–catalyst triple coupling for efficient nitrogen conversion and recovery in wastewater0Descriptor Identification and Synthesis of Bimetallic Catalysts for CO2 Methanation2Machine learning and catalyst engineering for sustainable hydrogen release from ammonia0Machine learning-driven optimization of ammonia decomposition catalysts: A multi-model comparative study with hyperparameter tuning and synthetic data augmentation1Harnessing Artificial Intelligence (AI) for a greener future: a review of AI advancements in green chemistry, chemical processes and sustainable materials3Catalyst and Catalysis Co-Exploration in Methane Utilization0Artificial Intelligence‐Assisted Design of High‐Entropy Oxide Catalysts0A Review on the Application of Machine Learning in Nitrogen Reduction Reaction0High-Throughput Experimentation Platform for Hydroxide Electrodeposition and Electrochemical Evaluation of Electrocatalysts0

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