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RESEARCH PAPER · Trends in Chemistry · 2021

Toward Machine Learning-Enhanced High-Throughput Experimentation

Natalie S. Eyke, Brent A. Koscher, Klavs F. Jensen

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Toward Machine Learning-Enhanced High-Throughput ExperimentationOriginalContinuous biphasic chemical processes in a four-phase segmented flow reactor10Toward autonomous design and synthesis of novel inorganic materials159Toward machine learning-enhanced high-throughput experimentation for chemistry14Autonomous flow reactors for discovery and invention29Cosmetic Chemistry1Machine learning and computation-enabled intelligent sensor design247Advancement in Organic Synthesis Through High Throughput Experimentation41Late-stage C–H functionalization offers new opportunities in drug discovery840Quick approach for optimization of monodisperse microsphere synthesis with a knowledge sharing strategy powered by machine learning2Technological Innovations in Photochemistry for Organic Synthesis: Flow Chemistry, High-Throughput Experimentation, Scale-up, and Photoelectrochemistry780Design and Application of a Screening Set for Monophosphine Lig-ands in Metal Catalysis7Mechanisms, challenges, and opportunities of dual Ni/ photoredox‐catalyzed C(sp 2 )–C(sp 3 ) cross‐couplings37Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization79High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research141Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains226High-throughput parallelized testing of membrane electrode assemblies for CO 2 reduction16Machine learning for design principles for single atom catalysts towards electrochemical reactions116Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Recent development in machine learning of polymer membranes for liquid separation31The Attention Economy of Materials AI: How Model Focus Shapes Scientific Attention Allocation0Third-harmonic Mie scattering from semiconductor nanohelices70High-throughput workflows in the service of (photo)electrocatalysis research14Interpretable machine learning for developing high-performance organic solar cells31Working at the interfaces of data science and synthetic electrochemistry21Autonomous Nanocrystal Doping by Self‐Driving Fluidic Micro‐Processors20Machine Learning–Assisted Design of Material Properties80HTE and Data Analysis for Discovery and Molecular-level Understanding of Catalysts0Machine Learning in Computational Chemistry1Autonomous high-throughput screening technology for accelerating drug molecule discovery and synthesis1Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling57Predicting Real‐Time Spectra from High‐Speed Imaging: An Ultrafast Machine Vision Framework for Online Optical Control in Microfluidics1Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models7Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models5Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory35Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning67Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing40Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling205Accelerated High-throughput Plant Imaging and Phenotyping System1Combination of Machine Learning and Analytical Correlations for Establishing Quantitative Compliance between the Trolox Equivalent Antioxidant Capacity Values Obtained via Electron Paramagnetic Resonance and Ultraviolet–Visible Spectroscopies5Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons19Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Implementation of High Throughput Experimentation across Medicinal Chemistry, Process Chemistry and Materials Science5Beyond Playing 20 Questions with Nature: Integrative Experiment Design in the Social and Behavioral Sciences55ACCELERATING DRUG DISCOVERY BY HIGH-THROUGHPUT EXPERIMENTATION1Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences90A field guide to flow chemistry for synthetic organic chemists393Accelerating reaction modeling using dynamic flow experiments, part 1: design space exploration21Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning55FDA path and process: Sponsor’s regulatory tasks for drug approval0HTE and machine learning-assisted development of iridium( i )-catalyzed selective O–H bond insertion reactions toward carboxymethyl ketones23Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1AROPS: A Framework of Automated Reaction Optimization with Parallelized Scheduling10Accelerated Electrosynthesis Development Enabled by High-Throughput Experimentation11Temperature-Controlled Photoreactors and ChemBeads as Key Technologies for Robust and Practical Photochemical HTE24Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Prediction of thermal diffusivity of volcanic rocks using machine learning and genetic algorithm hybrid strategy8Late-stage Functionalization for Improving Drug-like Molecular Properties264Accelerated high-throughput imaging and phenotyping system for small organisms10Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!37Accelerated discovery of oxidation-resistant ultra-high temperature ceramics via data driven methodology11Computational method for optimal electrolyte screening using Bayesian Optimization and physics based battery model0Introduction to High‐Throughput Experimentation (HTE) for the Synthetic Chemist11Development of high‐throughput wet‐chemical synthesis techniques for material research38Curated materials data of hybrid perovskites: approaches and potential usage3Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development38Machine learning and robot-assisted synthesis of diverse gold nanorods via seedless approach9Scope of machine learning in materials research—A review229EZ3micro - A Novel Miniaturized Pick & Place Prototype for Improved Industrial Applications2Accelerated chemical science with AI86Guiding experiment with Machine Learning: A case study of biochar adsorption of Ciprofloxacin40When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges64Discrete and mixed-variable experimental design with surrogate-based approach8Individual differences do matter0Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts26Regio-MPNN: predicting regioselectivity for general metal-catalyzed cross-coupling reactions using a chemical knowledge informed message passing neural network8Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry19Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation11Exploring solvation structure and transport behavior for rational design of advanced electrolytes for next generation of lithium batteries15Data-oriented protein kinase drug discovery10Electrochemical energy conversion and storage processes with machine learning18Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray8Data processing for high-throughput mass spectrometry in drug discovery10Computational Method for Optimal Electrolyte Screening Using Bayesian Optimization and Physics Based Battery Model0Design and Validation of a High-Throughput Reductive Catalytic Fractionation Method12Artificial intelligence in metabolomics: a current review118Toward soft robotic inspection for aircraft: An overview and perspective6Predictions of Chromatography Methods by Chemical Structure Similarity to Accelerate High-Throughput Medicinal Chemistry6From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design188Top 20 influential AI-based technologies in chemistry50Harnessing Compositional Gradients to Elucidate Phase Behaviors toward High Performance Polymer Semiconductor Blends3Self-Driving Laboratories for Chemistry and Materials Science663Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science19Machine learning-guided strategies for reaction conditions design and optimization48Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxides5Artificial intelligence and machine learning at various stages and scales of process systems engineering35Reaction blueprints and logical control flow for parallelized chiral synthesis in the Chemputer14Artificial Intelligence-Driven Development of Nickel-Catalyzed Enantioselective Cross-Coupling Reactions25Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning6Deep reinforcement learning for inverse inorganic materials design59Accelerating battery innovation: AI-powered molecular discovery58Computational tools for the prediction of site- and regioselectivity of organic reactions33Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space8Flow chemistry as a tool for high throughput experimentation15Reaction development: a student's checklist15Self-Driving Pharmaceutical Laboratories Need Explicit Rules for Exploration, Confirmation, Failure Recovery, Escalation, and Stopping0Dynamic Record Linking Using Multi-Agent Machine Learning: An Architecture for Noisy and Variable Datasets0Prof. Eric Jacobsen and #MSDChemistry: Past, present and future0Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes26Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies34The Implementation and Impact of Chemical High-Throughput Experimentation at AstraZeneca36Iron: From Basic Chemistry to Modern C∙H Bond Functionalization Catalysts1Using experimental data in computationally guided rational design of inorganic materials with machine learning7Machine learning accelerates high-voltage electrolyte discovery for lithium metal batteries21Swimming into the future: Machine learning in zebrafish behavioral research11Hydrogen Production from Catalytic Steam Reforming of Bio‐Oil: Recent Advances in Coke Resistance4Artificial intelligence in high-entropy materials9Statistics in enabling 2D materials: Optimization, predictive modelling, and data-driven discovery9Leveraging Data Science to Elucidate Ligand Features for Pd-Catalyzed Enantioretentive N-Arylations of Cyclic α-Substituted Amines in Aqueous Media4BiDir-GRCO: A Bidirectional General Reaction Conditions Optimization Framework Integrating Multi-Armed Bandit and Regression Model2Advancing Organic Chemistry Using High‐Throughput Experimentation26Advancing Organic Chemistry Using High‐Throughput Experimentation7Automating Synthetic Organic Electrochemistry: Concepts and Advancements6Discovering neural elastoplasticity from kinematic observations7Catalytic Resonance Theory: Experimental and Kinetic Interpretation of Programmable Catalysis5Bayesian Optimization in Polymer Modeling: From Coarse‐Graining Foundations to Autonomous Inverse Design5NOCTIS: open-source toolkit that turns reaction data into actionable graph networks0Solvent Sustainability in Drug Discovery: Where Are We Now, and How Can We Improve?24Artificial Intelligence and Other Emerging Technologies for Radiopharmaceutical Development0Artificial intelligence in research methodologies and challenges for mechanical and chemical engineering0Artificial Intelligence, Machine Learning, and High-Throughput Material Discovery Tools0Dielectric constant of polymers0Emerging trends and future directions in computer-aided drug design and artificial intelligence0Introduction to High-Entropy Alloys (HEAs)0Physics-interpreted closed-loop optimization and interpretable process insight in perovskite photovoltaics0Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films1The Impact of Continuous Flow Technology and Collaboration between Academia and Industry8High-throughput SEM imaging of nanoscale metal grains for heat assisted magnetic recording0From Prompt to Drug: Toward Pharmaceutical Superintelligence8Trends and Challenges in Multistep Continuous Flow Synthesis7Universal Baseline for in vitro Selection of Genetically Encoded Libraries0High-Throughput Electrochemistry Using Scanning Electrochemical Cells4Tailoring Materials Design for Aqueous Energy Storage and Conversion through Electrochemical Reconstruction20Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning3Nanoscale Direct-to-Biology Optimization of Cdk2 Inhibitors9Machine learning-assisted high-throughput screening of superlattice-like O-PCM thin films1Challenges and opportunities in machine learning for metal additive manufacturing: data scarcity and interpretability4Microfluidics: A Scalable Solution to Data Challenges in Chemical AI0Uncertainty-aware multi-stream deep learning for pediatric orthopedic bone age assessment from hand radiographs0Bioinspired (Glyco)Polypeptides for Ice‐Control and Cell Cryopreservation3From Data to Decision: Standardization, Validation, and Regulatory Readiness of AI-Driven Analytical Chemistry Workflows5

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