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RESEARCH PAPER · Chemical Science · 2018

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

Benjamin Meyer, Boodsarin Sawatlon, Stefan Heinen, O. Anatole von Lilienfeld, Clémence Corminbœuf

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Machine learning meets volcano plots: computational discovery of cross-coupling catalystsOriginalExpedited Screening of Active and Regioselective Catalysts for the Hydroformylation Reaction22Unraveling Metal/Pincer Ligand Effects in the Catalytic Hydrogenation of Carbon Dioxide to Formate40A quantitative uncertainty metric controls error in neural network-driven chemical discovery240Catalysis: energy is the measure of all things57Designing promising molecules for organic solar cells via machine learning assisted virtual screening115Exploring chemical space in the search for improved azoheteroarene-based photoswitches16Reaction mechanisms at the homogeneous–heterogeneous frontier: insights from first-principles studies on ligand-decorated metal nanoparticles44Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design61Moving Frontiers in Transition Metal Catalysis: Synthesis, Characterization and Modeling50Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models86Data Mining the C−C Cross‐Coupling Genome21Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists208Modeling the Catalyst Activation Step in a Metal–Ligand Radical Mechanism Based Water Oxidation System13Activity-Based Screening of Homogeneous Catalysts through the Rapid Assessment of Theoretically Derived Turnover Frequencies69Chemical diversity in molecular orbital energy predictions with kernel ridge regression78Chemical diversity in molecular orbital energy predictions with kernel ridge regression70Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver54Enumeration of de novo inorganic complexes for chemical discovery and machine learning42Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation90Beyond Density Functional Theory: The Multiconfigurational Approach To Model Heterogeneous Catalysis125Making machine learning a useful tool in the accelerated discovery of transition metal complexes47In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling75Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Gold‐Catalyzed Cross‐Coupling Reactions: An Overview of Design Strategies, Mechanistic Studies, and Applications192Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Quantum Chemistry Meets Machine Learning11Machine Learning in Catalysis, From Proposal to Practicing226Artificial Intelligence to Accelerate the Discovery of N 2 Electroreduction Catalysts103Data-powered augmented volcano plots for homogeneous catalysis36From Subnanometric Clusters toward Single-Atom Catalysts3High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex178Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization81Scaling relationships and volcano plots of homogeneous transition metal catalysis7Surface chemistry dictates stability and oxidation state of supported single metal catalyst atoms24The CSD and knowledge databases: from answers to questions22Automated in Silico Design of Homogeneous Catalysts186A Critical Review of Machine Learning of Energy Materials606An In‐Depth Mechanistic Study of Ru‐Catalysed Aqueous Methanol Dehydrogenation and Prospects for Future Catalyst Design34Atomic structures and orbital energies of 61,489 crystal-forming organic molecules101Density Functional Theories and Coordination Chemistry4Quantum Chemistry in the Age of Machine Learning417Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization198Machine learning the computational cost of quantum chemistry41Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions80Machine Learning in Chemistry77Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage67Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry62Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal Surfaces36Exploring chemical compound space with quantum-based machine learning369Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics47Representation of molecular structures with persistent homology for machine learning applications in chemistry121Turning chemistry into information for heterogeneous catalysis10Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost34Applications of Quantum Chemistry in Pharmaceutical Process Development: Current State and Opportunities48Using nature’s blueprint to expand catalysis with Earth-abundant metals592Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening58Machine learning corrected alchemical perturbation density functional theory for catalysis applications19Scaling Relationships and Volcano Plots in Homogeneous Catalysis48Probing Substrate Scope with Molecular Volcanoes16Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies33Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts51Accurate and rapid prediction of p K a of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach35Ab initio machine learning in chemical compound space4Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces16Integrated machine learning framework for computer-aided chemical product design2Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Towards the object-oriented design of active hydrogen evolution catalysts on single-atom alloys28Trends in computational molecular catalyst design27Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O223Machine learning for advanced energy materials208The Genesis of Molecular Volcano Plots121Method to Construct Volcano Relations by Multiscale Modeling: Building Bridges between the Catalysis and Biosimulation Communities2Computational catalysis for metal-organic frameworks: An overview67Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction164A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts21Grand Challenges in Computational Catalysis42Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From l inear‐scaling relationships to statistical learning techniques17Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold1Neuro-genetic machine learning framework accelerates the optimization of Ag/MnOx catalyst for total oxidation of toluene18Machine-learned potentials for next-generation matter simulations560Compact atomic descriptors enable accurate predictions via linear models25Perspective on integrating machine learning into computational chemistry and materials science203Catalyze Materials Science with Machine Learning67Integration 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 Systems840Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Ab Initio Machine Learning in Chemical Compound Space195Uncovering the Activity of Alkaline Earth Metal Hydrogenation Catalysis Through Molecular Volcano Plots7Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density38Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning33Methoxycyclization of 1,5‐Enynes by Coinage Metal Catalysts: Is Gold Always Superior?4Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning9Grand challenges on accelerating discovery in catalysis15The Evolution of Data-Driven Modeling in Organic Chemistry185A conceptual study of transfer learning with linear models for data-driven property prediction19Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge35A reactivity model for oxidative addition to palladium enables quantitative predictions for catalytic cross-coupling reactions63Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction42Machine learning for design principles for single atom catalysts towards electrochemical reactions116Machine learning the quantum flux–flux correlation function for catalytic surface reactions1Volcano Plots of Reaction Yields in Cross-Coupling Catalysis5Generalizing Performance Equations in Heterogeneous Catalysis from Hybrid Data and Statistical Learning18Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis65Representations and strategies for transferable machine learning improve model performance in chemical discovery19Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides10Roadmap on Machine learning in electronic structure180Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability93Emerging Landscape of Computational Modeling in Pharmaceutical Development48Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors122Genetic Optimization of Homogeneous Catalysts47Machine intelligence for chemical reaction space124Surface effect of the MgCl2 support in Ziegler–Natta catalyst for ethylene polymerization: A computational study49Comparative evaluation of artificial neural networks for the performance prediction of Pt‐based catalysts in water gas shift reaction12Machine Learning for the Discovery, Design, and Engineering of Materials66Assessing the persistence of chalcogen bonds in solution with neural network potentials13Deep dive into machine learning density functional theory for materials science and chemistry111Revising Message Passing Graph Neural Networks for Catalyst Design1New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts74Trendbericht Physikalische Chemie 2022: In‐situ‐Spektroskopie und Katalyse0Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis16Computational Catalyst Design with Data–Driven Tools – General Approaches and Applications1Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling57Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis18Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation45Machine learning-based analysis of overall stability constants of metal–ligand complexes31Current Outlooks on Machine Learning Methods for the Development of Industrial Homogeneous Catalytic Systems3Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes42When machine learning meets molecular synthesis56Electrocatalytic Oxygen Reduction to Produce Hydrogen Peroxide: Rational Design from Single-Atom Catalysts to Devices83Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization104Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Deep learning metal complex properties with natural quantum graphs59Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions50Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces53Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review19Zeolite encapsulated organometallic complexes as model catalysts3Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts10Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**40Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**13Computational Modeling of 4d and 5d Transition Metal Catalysts0Genetic Algorithms for the Discovery of Homogeneous Catalysts14The Role of AI in Driving the Sustainability of the Chemical Industry16Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study6Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery19

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