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RESEARCH PAPER · Nature · 2018

Controlling an organic synthesis robot with machine learning to search for new reactivity

Jarosław M. Granda, Liva Donina, Vincenza Dragone, De‐Liang Long, Leroy Cronin

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Controlling an organic synthesis robot with machine learning to search for new reactivityOriginalTransforming Computational Drug Discovery with Machine Learning and AI107Progress toward Solid State Synthesis by Design122Deep Learning for Nonadiabatic Excited-State Dynamics172Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds116High Throughput Strategies for the Discovery and Optimization of Catalytic Reactions165Algorithms for the self-optimisation of chemical reactions146The ubiquitous cross-coupling catalyst system ‘Pd(OAc) 2 ’/2PPh 3 forms a unique dinuclear Pd I complex: an important entry point into catalytically competent cyclic Pd 3 clusters88Evolving embodied intelligence from materials to machines161The importance of synthetic chemistry in the pharmaceutical industry668Progress in low-field benchtop NMR spectroscopy in chemical and biochemical analysis129Next-Generation Experimentation with Self-Driving Laboratories421Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals75Autonomous Molecular Design: Then and Now103New frontiers for the materials genome initiative562Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis333The digitization of organic synthesis122Low-field and benchtop NMR141Advancing Drug Discovery via Artificial Intelligence649Universal Chemical Synthesis and Discovery with ‘The Chemputer’154Synthetic organic chemistry driven by artificial intelligence303High-throughput screening for improved microbial cell factories, perspective and promise75Autonomous Discovery in the Chemical Sciences Part I: Progress317Flow Photochemistry: Shine Some Light on Those Tubes!381Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181Practical considerations for active machine learning in drug discovery95Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules124Cost, performance prediction and optimization of a vanadium flow battery by machine-learning122Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening108Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex178Machine learning the ropes: principles, applications and directions in synthetic chemistry279Perspectives from nearly five decades of total synthesis of natural products and their analogues for biology and medicine89Spectral deep learning for prediction and prospective validation of functional groups120A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation131Ternary Catalysis: A Stepping Stone toward Multicatalysis101Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties137A Hybrid Strategy for the Discovery and Design of Photonic Structures86Low‐field benchtop NMR spectroscopy: status and prospects in natural product analysis †120A curious formulation robot enables the discovery of a novel protocell behavior77Artificial Intelligence to Power the Future of Materials Science and Engineering154Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems258The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology150Closed-loop optimization of fast-charging protocols for batteries with machine learning1153Metasurface inverse design using machine learning approaches122A Structure-Based Platform for Predicting Chemical Reactivity288Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks112A map of the amine–carboxylic acid coupling system122A Bayesian experimental autonomous researcher for mechanical 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Science and Biomedicine189Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow95Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors169Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery94A review on machine learning algorithms for the ionic liquid chemical space196Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Toward Machine Learning-Enhanced High-Throughput Experimentation155Digital Transformation in Materials Science: A Paradigm Change in Material's Development72How Do Machines Learn? 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Standardized Reporting of Data**75Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare637Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab294Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Extending machine learning beyond interatomic potentials for predicting molecular properties167An all-round AI-Chemist with a scientific mind180Machine learning for a sustainable energy future517Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling208Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery138Machine-Learning-Guided Discovery of Electrochemical Reactions73Machine learning integrated photocatalysis: progress and challenges94Recent advances in the rational design of alkaline OER catalysts: from electronic structures to industrial applications74Perspectives for self-driving labs in synthetic biology115Chemical reaction networks and opportunities for machine learning141Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development343Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137The rise of self-driving labs in chemical and materials sciences570A Brief Introduction to Chemical Reaction Optimization437A robotic platform for the synthesis of colloidal nanocrystals169Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Generative Models as an Emerging Paradigm in the Chemical Sciences313Artificial Intelligence in Pharmaceutical Sciences132The central role of density functional theory in the AI age237MatGPT: A Vane of Materials Informatics from Past, Present, to Future74Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing90Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR397Autonomous chemical research with large language models1012Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept93Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory74Self-Driving Laboratories for Chemistry and Materials Science674A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand167

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