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RESEARCH PAPER · Nature Reviews Materials · 2018

Accelerating the discovery of materials for clean energy in the era of smart automation

Daniel P. Tabor, Loı̈c M. Roch, Semion K. Saikin, Christoph Kreisbeck, Dennis Sheberla, Joseph H. Montoya, Shyam Dwaraknath, Muratahan Aykol, C. Ortiz, Hermann Tribukait, Carlos Amador‐Bedolla, Christoph Josef Brabec, Benji Maruyama, Kristin Aslaug Persson, Alán Aspuru‐Guzik

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Accelerating the discovery of materials for clean energy in the era of smart automationOriginalThe Matter Simulation (R)evolution150Machine learning for heterogeneous catalyst design and discovery444ChemOS: Orchestrating autonomous experimentation180How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics316Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning722Puzzles and confusions in supercapacitor and battery: Theory and solutions360A Bayesian Approach to Predict Solubility Parameters94Quantum machine learning for electronic structure calculations145Practical use of polymer brushes in sustainable energy applications: interfacial nanoarchitectonics for high-efficiency devices169Deep learning for molecular design—a review of the state of the art677Machine learning for renewable energy materials312Progress and prospects for accelerating materials science with automated and autonomous workflows228Recent advances in nanomaterial-based synergistic combination cancer immunotherapy405Recent Advances in Rational Electrode Designs for High‐Performance Alkaline Rechargeable Batteries223Application of hydrides in hydrogen storage and compression: Achievements, outlook and perspectives852Solving the electronic structure problem with machine learning314SLIPS-TENG: robust triboelectric nanogenerator with optical and charge transparency using a slippery interface190Concepts for improving hydrogen storage in nanoporous materials261Next-Generation Experimentation with Self-Driving Laboratories421Classification of local chemical environments from x-ray absorption spectra using supervised machine learning107Autonomous Molecular Design: Then and Now103Toward Design of Novel Materials for Organic Electronics167Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks325A map of the inorganic ternary metal nitrides430Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm478Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning116Artificial intelligence for materials discovery120Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods357A robotic platform for flow synthesis of organic compounds informed by AI planning1052Trends in Solid Adsorbent Materials Development for CO 2 Capture352Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations99Autonomous Discovery in the Chemical Sciences Part II: Outlook245Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films127Silica Restricting the Sulfur Volatilization of Nickel Sulfide for High‐Performance Lithium‐Ion Batteries110Constrained Bayesian optimization for automatic chemical design using variational autoencoders339Machine Learning Approaches for Thermoelectric Materials Research231Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Autonomous intelligent agents for accelerated materials discovery95Cumulative charging behavior of water droplet driven freestanding triboelectric nanogenerators toward hydrodynamic energy harvesting104High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179QSAR without borders878Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191High-Throughput Process for the Discovery of Antimicrobial Polymers and Their Upscaled Production via Flow Polymerization96Automated in Silico Design of Homogeneous Catalysts186Carbon science perspective in 2020: Current research and future challenges108A Critical Review of Machine Learning of Energy Materials606Toward Accelerated Thermoelectric Materials and Process Discovery102Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems258Recent progress of biomass-derived carbon materials for supercapacitors460Closed-loop optimization of fast-charging protocols for batteries with machine learning1153Predicting the state of charge and health of batteries using data-driven machine learning784Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization198Mechanoluminescence materials for advanced artificial skin120Supported and coordinated single metal site electrocatalysts99ChemOS: An orchestration software to democratize autonomous discovery156Toward Excellence of Transition Metal‐Based Catalysts for CO2 Electrochemical Reduction: An Overview of Strategies and Rationales101Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab171Opportunities and Challenges for Machine Learning in Materials Science523Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses127Robot-Accelerated Perovskite Investigation and Discovery187Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Materials Acceleration Platforms: On the way to autonomous experimentation149Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions120A mobile robotic chemist1487Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships171AI Applications through the Whole Life Cycle of Material Discovery192Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells154Carbon–based transition metal sulfides/selenides nanostructures for electrocatalytic water splitting109Designing and understanding light-harvesting devices with machine learning112When artificial intelligence meets building energy efficiency, a review focusing on zero energy building110Novel Heterogeneous Catalysts for CO 2 Hydrogenation to Liquid Fuels361Solid state chemistry for developing better metal-ion batteries259Autonomous materials synthesis by machine learning and robotics188Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning186Emerging materials intelligence ecosystems propelled by machine learning441On-the-fly closed-loop materials discovery via Bayesian active learning542Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow95Accelerating organic solar cell material's discovery: high-throughput screening and big data129Toward autonomous design and synthesis of novel inorganic materials159Designing heat transfer pathways for advanced thermoregulatory textiles111Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning171Recent progress in conjugated microporous polymers for clean energy: Synthesis, modification, computer simulations, and applications191Reaction kinetics in rechargeable zinc-ion batteries97Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime264Machine learning in materials science: From explainable predictions to autonomous design281Fibre-based composites from the integration of metal–organic frameworks and polymers301Toward smart carbon capture with machine learning121Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer148Open Catalyst 2020 (OC20) Dataset and Community Challenges765Machine-learned potentials for next-generation matter simulations560Nobel Turing Challenge: creating the engine for scientific discovery140Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator128G ryffin : An algorithm for Bayesian optimization of categorical variables informed by expert knowledge170A Meta‐Analysis of Conductive and Strong Carbon Nanotube Materials174Autonomous experimentation systems for materials development: A community perspective398Data-science driven autonomous process optimization226Semiconductor quantum dots: Technological progress and future challenges1962Recent developments in polydopamine-based photocatalytic nanocomposites for energy production: Physico-chemical properties and perspectives104Gaussian Process Regression for Materials and Molecules1221Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis155Machine learning for high-throughput experimental exploration of metal halide perovskites117Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives151Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols128High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107Current Topics in Ionic Liquid Crystals95Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data198Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams98Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments1984From Platform to Knowledge Graph: Evolution of Laboratory Automation108Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials166A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+235MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**237Review of computational approaches to predict the thermodynamic stability of inorganic solids196A self-driving laboratory advances the Pareto front for material properties211Interpretable machine learning for knowledge generation in heterogeneous catalysis428New black indium oxide—tandem photothermal CO2-H2 methanol selective catalyst163Machine learning in energy storage materials106Materials, physics and systems for multicaloric cooling288Data‐Driven Materials Innovation and Applications187Accelerating materials discovery using artificial intelligence, high performance computing and robotics462Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective126Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137An all-round AI-Chemist with a scientific mind180Principles of the Battery Data Genome132Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing133Graph neural networks for materials science and chemistry860How machine learning can accelerate electrocatalysis discovery and optimization125Artificial intelligence and machine learning in energy systems: A bibliographic perspective385Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137The rise of self-driving labs in chemical and materials sciences570Recent Advances in Thermoregulatory Clothing: Materials, Mechanisms, and Perspectives228A robotic platform for the synthesis of colloidal nanocrystals169Generative Models as an Emerging Paradigm in the Chemical Sciences313Recent Progresses in Machine Learning Assisted Raman Spectroscopy250Fabrication of biomass-based functional carbon materials for energy conversion and storage123Recent applications of machine learning in alloy design: A review158Accelerating the prediction of stable materials with machine learning104Scaling deep learning for materials discovery1473The future of self-driving laboratories: from human in the loop interactive AI to gamification102A dynamic knowledge graph approach to distributed self-driving laboratories108Sustainable moisture energy184Electronic Structure Design of Transition Metal-Based Catalysts for Electrochemical Carbon Dioxide Reduction119Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments139Emerging Photoreforming Process to Hydrogen Production: A Future Energy106Integrating artificial intelligence in energy transition: A comprehensive review248

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