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RESEARCH PAPER · Angewandte Chemie International Edition · 2019

Autonomous Discovery in the Chemical Sciences Part I: Progress

Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen

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Autonomous Discovery in the Chemical Sciences Part I: ProgressOriginalA streamlined synthesis of the neurosteroid 3β-methoxypregnenolone assisted by a statistical experimental design and automation6AI-assisted synthesis prediction55Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening57The good, the bad, and the ugly in chemical and biological data for machine learning54When robotics met fluidics38Benchmarking the acceleration of materials discovery by sequential learning151Inverse methods for design of soft materials94Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge64Machine‐learning scoring functions for structure‐based virtual screening163Automation of Synthesis in Medicinal Chemistry: Progress and Challenges35Organic Superbases in Recent Synthetic Methodology Research116Molecular representations in AI-driven drug discovery: a review and practical guide576Flow Synthesis of Metal Halide Perovskite Quantum Dots: From Rapid Parameter Space Mapping to AI-Guided Modular Manufacturing84Adaptive Optimization of Chemical Reactions with Minimal Experimental Information82Deep Learning in Protein Structural Modeling and Design227A Recommender System for Inverse Design of Polycarbonates and Polyesters28Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model24Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently55Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors169Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors9Bayesian optimization of nanoporous materials111Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules113Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells12Data-driven algorithms for inverse design of polymers100Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials31Golem: an algorithm for robust experiment and process optimization39Graph neural networks and high throughput quantum chemistry workflows for detailed kinetic modeling1High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Toward Machine Learning-Enhanced High-Throughput Experimentation155Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases62Active learning accelerates ab initio molecular dynamics on reactive energy surfaces93Comment on acp-2020-12580Comment on acp-2020-12580Ready, Set, Flow! Automated Continuous Synthesis and Optimization134Olympus: a benchmarking framework for noisy optimization and experiment planning69Comment on acp-2020-12580Predicting Reaction Yields via Supervised Learning187Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion19Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Active discovery of organic semiconductors136Evaluation of categorical matrix completion algorithms: toward improved active learning for drug discovery5Reply on RC10Reply on RC20Reply on RC30Inferring experimental procedures from text-based representations of chemical reactions81Future directions of chemical theory and computation6Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems842Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354G ryffin : An algorithm for Bayesian optimization of categorical variables informed by expert knowledge170Designing and Implementing VR 2 E 2 C, a Virtual Reality Remote Education for Experimental Chemistry System39Data-science driven autonomous process optimization228Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning6Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space71Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network76Exploring Multidimensional Chemical Spaces: Instrumentation and Chemical Systems for the Parallelization of Hydrogen Evolving Photocatalytic Reactions13Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment22Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning33Mechanisms, challenges, and opportunities of dual Ni/ photoredox‐catalyzed C(sp 2 )–C(sp 3 ) cross‐couplings37Toward Predictive Chemical Deformulation Enabled by Deep Generative Neural Networks7GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics21Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units**12Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration5Recent advances in autonomous synthesis of materials33The Evolution of Data-Driven Modeling in Organic Chemistry185Transition to sustainable chemistry through digitalization85Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization79Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge15High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research141Coming of Age of Computational Chemistry from a Resilient Past to a Promising Future2Generating molecules with optimized aqueous solubility using iterative graph translation15Modern nanoscience: Convergence of AI, robotics, and colloidal synthesis45Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells39Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective376Progress towards machine learning reaction rate constants58Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization37Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity29Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation101Bayesian optimization with known experimental and design constraints for chemistry applications89Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids50Extending BigSMILES to non-covalent bonds in supramolecular polymer assemblies33Predicting reaction conditions from limited data through active transfer learning78Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46The effect of chemical representation on active machine learning towards closed-loop optimization53Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources191Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration74From Platform to Knowledge Graph: Evolution of Laboratory Automation108A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis310Third-harmonic Mie scattering from semiconductor nanohelices70Toward autonomous materials research: Recent progress and future challenges60Towards artificial intelligence at scale in the chemical industry69Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering42Agents for sequential learning using multiple-fidelity data38Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes7Exploration of Ultralarge Compound Collections for Drug Discovery150The case for data science in experimental chemistry: examples and recommendations83On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning55Defining Levels of Automated Chemical Design44Machine Learning for Chemical Reactivity: The Importance of Failed Experiments218Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente8Autonomous chemical science and engineering enabled by self-driving laboratories72Recognition-Enabled Automated Analyte Identification via 19F NMR15High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective16Analytical Tools Integrated in Continuous-Flow Reactors: Which One for What?64Advancing Base Metal Catalysis through Data Science: Insight and Predictive Models for Ni-Catalyzed Borylation through Supervised Machine Learning24Chemical engineering at crossroads4Mechanistic Inference from Statistical Models at Different Data-Size Regimes37An extensible software platform for accelerating polymer discovery through informatics and artificial intelligence development5Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors293Computer‐Assisted Design of Sustainable Syntheses of Pharmaceuticals and Agrochemicals from Industrial Wastes0Graph-based Automated Macro-Molecule Assembly0Scanning probes as a materials automation platform with extremely miniaturized samples8Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach42Networks and interfaces as catalysts for polymer materials innovation10Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization104Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives7Graph machine learning for design of high‐octane fuels36Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery139What is a minimal working example for a self-driving laboratory?49Autonomous continuous flow reactor synthesis for scalable atom-precision26Bayesian optimisation for additive screening and yieldimprovements in chemical reactions – beyond one-hot encodings2Autonomous Drug Design with Multi-Armed Bandits7Efficient design of peptide-binding polymers using active learning approaches13A transferable recommender approach for selecting the best density functional approximations in chemical discovery31Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery51A field guide to flow chemistry for synthetic organic chemists393Autonomous kinetic model identification using optimal experimental design and retrospective data analysis: methane complete oxidation as a case study13By how much can closed-loop frameworks accelerate computational materials discovery?21Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization33First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis48Integrating autonomy into automated research platforms32Machine learning integrated photocatalysis: progress and challenges93Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches7Quantifying the performance of machine learning models in materials discovery47Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1Synthetic data enable experiments in atomistic machine learning16What is missing in autonomous discovery: open challenges for the community44Δ 2 machine learning for reaction property prediction28Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols39Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols1Automated Library Generation and Serendipity Quantification Enables Diverse Discovery in Coordination Chemistry12A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.075Electrochemical Carbon-Ferrier Rearrangement Using a Microflow Reactor and Machine Learning-Assisted Exploration of Suitable Conditions18Physics‐informed Transfer Learning for Out‐of‐sample Vapor Pressure Predictions19Enabling technology and core theory of synthetic biology65Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening34Self-driving laboratories: A paradigm shift in nanomedicine development84

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