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RESEARCH PAPER · Nature · 2020

A mobile robotic chemist

Benjamin Burger, Phillip M. Maffettone, Vladimir V. Gusev, Catherine M. Aitchison, Yang Bai, Xiaoyan Wang, Xiaobo Li, Ben M. Alston, Buyi Li, Rob Clowes, Nicola Rankin, Brandon Harris, Reiner Sebastian Sprick, Andrew Ian Cooper

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42 direct citing works

A mobile robotic chemistOriginalElectrocatalyst design for aprotic Li–CO 2 batteries115Digital Reticular Chemistry171Time Economy in Total Synthesis136Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future185Artificial intelligence and automation in computer aided synthesis planning88The Role of Machine Learning in the Understanding and Design of Materials433Automation in the Life Science Research Laboratory200Porous flexible frameworks: origins of flexibility and applications114Machine learning for metabolic engineering: A review313On-the-fly closed-loop materials discovery via Bayesian active learning542Fully Exposed Cluster Catalyst (FECC): Toward Rich Surface Sites and Full Atom Utilization Efficiency305Materials design by synthetic biology386Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery94Accelerating organic solar cell material's discovery: high-throughput screening and big data129Advancing photoreforming of organics: highlights on photocatalyst and system designs for selective oxidation reactions262Atomic-level insight into reasonable design of metal-based catalysts for hydrogen oxidation in alkaline electrolytes143Automation isn't automatic131Bayesian optimization of nanoporous materials112Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules113Data-driven algorithms for inverse design of polymers100Organic materials as photocatalysts for water splitting100Recent trends in biocatalysis383Toward autonomous design and synthesis of novel inorganic materials159Toward Machine Learning-Enhanced High-Throughput Experimentation155Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning171Automated Experimentation Powers Data Science in Chemistry111Machine learning for advanced energy materials208A data fusion approach to optimize compositional stability of halide perovskites158Data-Driven Strategies for Accelerated Materials Design473Bayesian reaction optimization as a tool for chemical synthesis1044General synthetic strategy for regioselective ultrafast formation of disulfide bonds in peptides and proteins91Catalytic Conversion of Carbon Dioxide to Methanol: Current Status and Future Perspective90Recent Advances in Silent Gene Cluster Activation in Streptomyces139Progress in robotics for combating infectious diseases121Automation and computer-assisted planning for chemical synthesis189Diversity-oriented synthesis of polymer membranes with ion solvation cages143Crystallography companion agent for high-throughput materials discovery99Two-step machine learning enables optimized nanoparticle synthesis267An updated status and trends in actinide metal-organic frameworks (An-MOFs): From synthesis to application145Nanoparticle synthesis assisted by machine learning545Autonomous experimentation systems for materials development: A community perspective398End-of-life or second-life options for retired electric vehicle batteries302Data-science driven autonomous process optimization226Computational pharmaceutics - A new paradigm of drug delivery218Carbon Nitride Thin Films as All-In-One Technology for Photocatalysis100Accelerating antibiotic discovery through artificial intelligence227Innovative Materials Science via Machine Learning194Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Accelerated discovery of 3D printing materials using data-driven multiobjective optimization134Machine learning for high-throughput experimental exploration of metal halide perovskites117Artificial intelligence: A powerful paradigm for scientific research1702High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107Optical/electrochemical methods for detecting mitochondrial energy metabolism118Storing energy with molecular photoisomers253Flexible automation accelerates materials discovery98Data-Driven Methods for Accelerating Polymer Design123Bayesian optimization with known experimental and design constraints for chemistry applications89Machine learning for design principles for single atom catalysts towards electrochemical reactions116Preparation, characterization, evaluation and mechanistic study of organic polymer nano-photocatalysts for solar fuel production98The order–disorder conundrum: a trade-off between crystalline and amorphous porous organic polymers for task-specific applications128From Platform to Knowledge Graph: Evolution of Laboratory Automation108Semiconducting Polymers for Oxygen Evolution Reaction under Light Illumination338Monitoring the Degree of Comfort of Shoes In-Motion Using Triboelectric Pressure Sensors with an Ultrawide Detection Range195A self-driving laboratory advances the Pareto front for material properties211Methane transformation by photocatalysis439Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction91Machine intelligence for chemical reaction space124Artificial intelligence to bring nanomedicine to life189Emerging Strategies for CO 2 Photoreduction to CH 4 : From Experimental to Data‐Driven Design151Intelligent disassembly of electric-vehicle batteries: a forward-looking overview172The living interface between synthetic biology and biomaterial design218Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing217Data‐Driven Materials Innovation and Applications187Accelerating materials discovery using artificial intelligence, high performance computing and robotics462Reaction Pathways toward Sustainable Photosynthesis of Hydrogen Peroxide by Polymer Photocatalysts244The 2022 solar fuels roadmap150Robotic search for optimal cell culture in regenerative medicine99Evaluation guidelines for machine learning tools in the chemical sciences180Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review171Digitization and validation of a chemical synthesis literature database in the ChemPU89Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors294Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Renewed Prospects for Organic Photovoltaics1052Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab294Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137An all-round AI-Chemist with a scientific mind180Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling206An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials192On scientific understanding with artificial intelligence372Machine learning for a sustainable energy future517Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Merging molecular catalysts and metal–organic frameworks for photocatalytic fuel production149How machine learning can accelerate electrocatalysis discovery and optimization125Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences90Insights into the solvation chemistry in liquid electrolytes for lithium-based rechargeable batteries443Machine learning integrated photocatalysis: progress and challenges94Perspectives for self-driving labs in synthetic biology115Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137Organic reaction mechanism classification using machine learning148The rise of self-driving labs in chemical and materials sciences570Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170A robotic platform for the synthesis of colloidal nanocrystals169Combinatorial synthesis for AI-driven materials discovery114Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning215Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217Generative Models as an Emerging Paradigm in the Chemical Sciences313Machine Learning Descriptors for Data‐Driven Catalysis Study154AI-powered therapeutic target discovery332Machine Learning for Chemistry: Basics and Applications102Key Strategies for Enhancing H 2 Production in Transition Metal Oxide Based Photocatalysts91In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Revolutionizing drug formulation development: The increasing impact of machine learning128Large language models for chemistry robotics110AI-driven robotic chemist for autonomous synthesis of organic molecules108Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells89ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs168Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist129Vision-controlled jetting for composite systems and robots135An autonomous laboratory for the accelerated synthesis of inorganic materials1000Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR397Autonomous chemical research with large language models1012Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back172Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry104Automation and machine learning augmented by large language models in a catalysis study107Chemistries and materials for atmospheric water harvesting135Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept93The future of self-driving laboratories: from human in the loop interactive AI to gamification102Understanding charge carrier dynamics in organic photocatalysts for hydrogen evolution119Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation239Self-driving laboratories to autonomously navigate the protein fitness landscape207A dynamic knowledge graph approach to distributed self-driving laboratories108The rise of high-entropy battery materials176Analysis of metal–organic framework-based photosynthetic CO2 reduction152Halide Perovskites for Photoelectrochemical Water Splitting and CO2 Reduction: Challenges and Opportunities89Autonomous chemistry: Navigating self-driving labs in chemical and material sciences95Self-Driving Laboratories for Chemistry and Materials Science674Autonomous mobile robots for exploratory synthetic chemistry215ORGANA: A robotic assistant for automated chemistry experimentation and characterization131Tool Learning with Foundation Models177AI-driven 3D bioprinting for regenerative medicine: From bench to bedside101A review of large language models and autonomous agents in chemistry269Enrichment strategies in surface-enhanced Raman scattering: theoretical insights and optical design for enhanced light-matter interaction90Artificial Intelligence-Powered Materials Science116Identifying a highly efficient molecular photocatalytic CO2 reduction system via descriptor-based high-throughput screening96A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand167A generalized platform for artificial intelligence-powered autonomous enzyme engineering112Accelerated data-driven materials science with the Materials Project300Exploring the role of large language models in the scientific method: from hypothesis to discovery89Artificial intelligence tools expand scientists’ impact but contract science’s focus104

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