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RESEARCH PAPER · Science · 2019

Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Andrew F. Zahrt, Jeremy J. Henle, Brennan T. Rose, Yang Wang, William T. Darrow, Scott E. Denmark

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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learningOriginalEnantioselective C H Bond Functionalizations by 3d Transition-Metal Catalysts271The digitization of organic synthesis122A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning120Holistic prediction of enantioselectivity in asymmetric catalysis321Towards the online computer-aided design of catalytic pockets1279Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245DScribe: Library of descriptors for machine learning in materials science845Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence65CO2 hydrogenation to high-value products via heterogeneous catalysis1176Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future211High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex178Machine learning the ropes: principles, applications and directions in synthetic chemistry279Progress in the Application of Machine Learning in Combustion Studies51Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization81What can reaction databases teach us about Buchwald–Hartwig cross-couplings?64A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation131Automated in Silico Design of Homogeneous Catalysts186Artificial Intelligence to Power the Future of Materials Science and Engineering154A Structure-Based Platform for Predicting Chemical Reactivity288Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation70A map of the amine–carboxylic acid coupling system122Accelerated Discovery of Potential Organic Dyes for Dye‐Sensitized Solar Cells by Interpretable Machine Learning Models and Virtual Screening58Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides66Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning130Molecular Representation: Going Long on Fingerprints114Machine Learning in Chemistry77Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage67Development of a Computer-Guided Workflow for Catalyst Optimization. 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novel machine learning method for evaluating the impact of emission sources on ozone formation62Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Machine-Learning-Guided Discovery of Electrochemical Reactions73A review on the application of molecular descriptors and machine learning in polymer design108Designing catalysts with deep generative models and computational data. 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