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RESEARCH PAPER · Trends in Chemistry · 2019

Next-Generation Experimentation with Self-Driving Laboratories

Florian Häse, Loı̈c M. Roch, Alán Aspuru‐Guzik

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Next-Generation Experimentation with Self-Driving LaboratoriesOriginalProgress and prospects for accelerating materials science with automated and autonomous workflows228Systematic study of TiO 2 /ZnO mixed metal oxides for CO 2 photoreduction27The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation0Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management73The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation123Universal Chemical Synthesis and Discovery with ‘The Chemputer’154Machine learning for target discovery in drug development61A streamlined synthesis of the neurosteroid 3β-methoxypregnenolone assisted by a statistical experimental design and automation6When robotics met fluidics38Benchmarking the acceleration of materials discovery by sequential learning149Machine-enabled inverse design of inorganic solid materials: promises and challenges190QSAR without borders878Synthetic approaches toward small molecule libraries5Exploring Novel Biologically-Relevant Chemical Space Through Artificial Intelligence: The NCATS ASPIRE Program5Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots76The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology150Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation78Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset0Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset1Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation70Autonomous Titration for Chemistry Classrooms: Preparing Students for Digitized Chemistry Laboratories4Autonomous Titration for Chemistry Classrooms: Preparing Students for Digitized Chemistry Laboratories0ChemOS: An orchestration software to democratize autonomous discovery156An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge60An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge12From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning16Opportunities and Challenges for Machine Learning in Materials Science523Self-driving laboratory for accelerated discovery of thin-film materials809The prospects of quantum computing in computational molecular biology215Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Flow chemistry remains an opportunity for chemists and chemical engineers66Materials Acceleration Platforms: On the way to autonomous experimentation149Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition107A Prediction of Future States: AI-powered Chemical Innovation for Defense Applications2Artificial Intelligence Applied to the Prediction of Organic Materials12Machine Learning at the (Nano)materials-biology Interface4Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN61Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships171Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set74RetroPrime: A Chemistry-Inspired and Transformer-based Method for Retrosynthesis Predictions3A machine learning Automated Recommendation Tool for synthetic biology277Unsupervised Phase Discovery with Deep Anomaly Detection103Bias Free Multiobjective Active Learning for Materials Design and Discovery12The Role of Machine Learning in the Understanding and Design of Materials433The Blockchain Integrated Automatic Experiment Platform (BiaeP)8Machine learning for metabolic engineering: A review313A Recommender System for Inverse Design of Polycarbonates and Polyesters28Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently55Accelerating organic solar cell material's discovery: high-throughput screening and big data129Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells12DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52First-Principles Calculations of Stability, Electronic Structure, and Sorption Properties of Nanoparticle Systems1Golem: an algorithm for robust experiment and process optimization38Impact of Blended Approach on Second Year Home Economics Students’ Biology Practical Achievement in Two Districts of Oti Region, Ghana1Toward Machine Learning-Enhanced High-Throughput Experimentation155Future medicinal chemists experience flow chemistry: optimization by experimental design of the limiting synthetic step to the antifungal drug econazole nitrate3Identification of the dominant recombination process for perovskite solar cells based on machine learning61Data-Driven Strategies for Accelerated Materials Design473Bayesian reaction optimization as a tool for chemical synthesis1044Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals54100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design40Data Science, Machine Learning and Artificial Intelligence Applied to Metals and Alloys Research: Past, Present, and Future3A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery10A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery3Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions72Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation1Lessons learned from device modeling of organic & perovskite solar cells0Determining the Activity Series with the Fewest Experiments Using Sorting Algorithms1Machine learning in materials science: From explainable predictions to autonomous design281Olympus: a benchmarking framework for noisy optimization and experiment planning69Data Science in Chemical Engineering: Applications to Molecular Science20Machine learning for quantum physics and quantum physics for machine learning1Diversity-oriented synthesis of polymer membranes with ion solvation cages143Bias free multiobjective active learning for materials design and discovery198RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions129Machine learning in clinical decision making282AI tool makes phase identification crystal clear1Machine learning directed drug formulation development335Advancing the Pareto front for thin-film materials using a self-driving laboratory0Advancing the Pareto front using a self-driving laboratory2Nobel Turing Challenge: creating the engine for scientific discovery140The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62Ethical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability23Machine learning elastic constants of multi-component alloys68G ryffin : An algorithm for Bayesian optimization of categorical variables informed by expert knowledge170Standardisierung und Kontrolle von Grignard‐Reaktionen mittels Online‐NMR in einer universellen chemischen Syntheseplattform1Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR29Autonomous experimentation systems for materials development: A community perspective398Data-science driven autonomous process optimization226Technological Innovations in Photochemistry for Organic Synthesis: Flow Chemistry, High-Throughput Experimentation, Scale-up, and Photoelectrochemistry781Artificial Intelligence Applied to Battery Research: Hype or Reality?503Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives151Re‐imagining Priorities for Chemistry: A Central Science for “Freedom from Fear and Want”27Re‐imagining Priorities for Chemistry: A Central Science for “Freedom from Fear and Want”2Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains225Safe Reinforcement Learning in Simulated Environment of Self-Driving Laboratory0Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials23Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs4Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization37A Conceptual Theory of Model-Science Interface: Where AI Outputs Become Experimental Inputs0A forward view for Digital Discovery : the scientific challenges of the twenty-first century require accelerated discovery approaches5Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence62Ali nas umetna inteligenca lahko premaga: od algoritma do singularnosti po poteh etičnega vrednotenja0Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation30Autonomous model-based experimental design for rapid reaction development25Avtonomna orožja in erozija moralne odgovornosti: sistematizacija nasilja in izginjanje pravičnosti0Bayesian optimization with known experimental and design constraints for chemistry applications89Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research21Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids50Od Descartesa do Alexe: filozofski pogled na razvoj umetne inteligence0Po sledeh umetne inteligence: kaj nam o psiholoških lastnostih posameznikov povedo njihovi digitalni odtisi?0Raziskovalna umetna inteligenca in standardi transparentnosti0Robotic cell assembly to accelerate battery research46Socialni odnosi, animizem, antropomorfizem in interakcija z UI11Sodobne perspektive družbe: Umetna inteligenca na stičišču znanosti1The Treatment of Absence and Null Results in Materials Machine Learning Literature: A Review Study0Transformacija 'inteligence roja' {Swarm Intelligence} na umetno inteligenco0Transparentnost in razložljivost kot zahtevi za zaupanja vredno umetno inteligenco0Umetna inteligenca in eksistenčno tveganje0Vloga umetne inteligence v izobraževanju in za izobraževanje3Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments1984From Platform to Knowledge Graph: Evolution of Laboratory Automation108Three-dimensional modeling of Lithium-ion battery electrode manufacturing0Toward autonomous materials research: Recent progress and future challenges61Active meta-learning for predicting and selecting perovskite crystallization experiments24Autonomous Multi‐Step and Multi‐Objective Optimization Facilitated by Real‐Time Process Analytics85Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate24A self-driving laboratory advances the Pareto front for material properties211Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering42Scalable and automated CRISPR-based strain engineering using droplet microfluidics39Digital Twins for Materials90Artificial intelligence for synthetic biology35Promoting Sustainability through Next-Generation Biologics Drug Development8Data‐Driven Materials Innovation and Applications187MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research15Theory-guided experimental design in battery materials research156An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics45Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform180Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Quantifying and rationalizing polarization curves of Zn-air fuel-cells: A simple enabling contribution to device-scale analysis and monitoring7THE IMPACT OF INDUSTRY 4.0 ON THE FORMATION OF SCIENCE 4.05Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab294Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Brokering between tenants for an international materials acceleration platform5High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques42Natural Language Processing Challenges and Issues: A Literature Review29Investigating quantum many-body systems with tensor networks, machine learning and quantum computers0Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds101Bayesian optimization of electrochemical devices for electrons-to-molecules conversions: the case of pulsed CO 2 electroreduction17A perspective on Bayesian methods applied to materials discovery and design47

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