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RESEARCH PAPER · Nature Communications · 2020

On-the-fly closed-loop materials discovery via Bayesian active learning

A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo A, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi

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On-the-fly closed-loop materials discovery via Bayesian active learningOriginalHigh-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials19Toward autonomous design and synthesis of novel inorganic materials159Machine learning for advanced energy materials208Materials representation and transfer learning for multi-property prediction71Spectral Tomography for 3D Element Detection and Mineral Analysis22JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics85Deep learning for visualization and novelty detection in large X-ray diffraction datasets63Computational design of moiré assemblies aided by artificial intelligence22Bonding nature and optical contrast of TiTe2/Sb2Te3 phase-change heterostructure21Machine learning for alloys698Autonomous experimentation systems for materials development: A community perspective398Machine learning for materials discovery: Two-dimensional topological insulators71Automated stopping criterion for spectral measurements with active learning28Automating crystal-structure phase mapping by combining deep learning with constraint reasoning68Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data29Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis155Artificial intelligence for search and discovery of quantum materials68Machine learning for multiscale modeling in computational molecular design25Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development19On the importance of microstructure information in materials design: PSP vs PP29Bayesian optimization with adaptive surrogate models for automated experimental design138Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams98Bayesian optimization with known experimental and design constraints for chemistry applications89Benchmarking active learning strategies for materials optimization and discovery48Improving prediction accuracy of high-performance materials via modified machine learning strategy34Predicting the property contour-map and optimum composition of Cu-Co-Si alloys via machine learning20Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning32Toward autonomous materials research: Recent progress and future challenges61Active meta-learning for predicting and selecting perovskite crystallization experiments24Photonic and optoelectronic neuromorphic computing85Sample-efficient parameter exploration of the powder film drying process using experiment-based Bayesian optimization20Roadmap on Machine learning in electronic structure180Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries72Agents for sequential learning using multiple-fidelity data38Materials, physics and systems for multicaloric cooling288Active learning and molecular dynamics simulations to find high melting temperature alloys40Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration91Accelerated materials design using batch Bayesian optimization: A case study for solving the inverse problem from materials microstructure to process specification45The case for data science in experimental chemistry: examples and recommendations83Accelerating materials discovery using artificial intelligence, high performance computing and robotics462Discovery of Pb-free hybrid organic–inorganic 2D perovskites using a stepwise optimization strategy24Deep learning to catalyze inverse molecular design29On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning55Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping31XGBoost model for electrocaloric temperature change prediction in ceramics70A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys33Designing lattices for impact protection using transfer learning33Bayesian optimization with experimental failure for high-throughput materials growth53Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures - A review86Why big data and compute are not necessarily the path to big materials science51The LEGOLAS Kit: A low-cost robot science kit for education with symbolic regression for hypothesis discovery and validation19Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling206Linking scientific instruments and computation: Patterns, technologies, and experiences45Machine‐Learning Spectral Indicators of Topology35An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials192Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Artificial intelligence for materials research at extremes20Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118aflow++: A C++ framework for autonomous materials design42Uncertainty-aware mixed-variable machine learning for materials design37By how much can closed-loop frameworks accelerate computational materials discovery?21Integrating autonomy into automated research platforms32Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations41An integrated chemical engineering approach to understanding microplastics23Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence43Emerging Trends in Machine Learning: A Polymer Perspective187The Autonomous Formulation Laboratory: An Open Liquid Handling Platform for Formulation Discovery Using X-ray and Neutron Scattering52The rise of self-driving labs in chemical and materials sciences570Energy Efficient Neuro‐Inspired Phase–Change Memory Based on Ge 4 Sb 6 Te 7 as a Novel Epitaxial Nanocomposite46Next-generation intelligent laboratories for materials design and manufacturing25Role of AI in experimental materials science42Combinatorial synthesis for AI-driven materials discovery114Biological research and self-driving labs in deep space supported by artificial intelligence49Small data machine learning in materials science866Bayesian optimization with active learning of design constraints using an entropy-based approach132ET-AL: Entropy-targeted active learning for bias mitigation in materials data30Scalable multi-agent lab framework for lab optimization33Integrated Memristor Network for Physiological Signal Processing46Active learning-assisted neutron spectroscopy with log-Gaussian processes20Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Physics-Informed Bayesian learning of electrohydrodynamic polymer jet printing dynamics24Integrated optical memristors187Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!37Scientific discovery in the age of artificial intelligence1779Non-volatile tunable optics by design: From chalcogenide phase-change materials to device structures42Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments37In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99A Survey on Collaborative Learning for Intelligent Autonomous Systems35MatGPT: A Vane of Materials Informatics from Past, Present, to Future74Autonomous and dynamic precursor selection for solid-state materials synthesis47Accelerating the prediction of stable materials with machine learning104Exploiting redundancy in large materials datasets for efficient machine learning with less data118Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization37Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows22Inverse design of high entropy alloys using a deep interpretable scheme for materials attribution analysis21AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories33Development and application of Few-shot learning methods in materials science under data scarcity47Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange52Machine learning assisted layer-controlled synthesis of MoS 225Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept93Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures74Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory97On active learning for Gaussian process-based global sensitivity analysis32Human-in-the-loop for Bayesian autonomous materials phase mapping25Designing workflows for materials characterization22Machine learning for CO 2 capture and conversion: A review60An active machine learning approach for optimal design of magnesium alloys using Bayesian optimisation31Navigating phase diagram complexity to guide robotic inorganic materials synthesis82ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories103Compositional design of multicomponent alloys using reinforcement learning45Roadmap on data-centric materials science27Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery29Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review50GP+: A Python library for kernel-based learning via Gaussian processes26Autonomous chemistry: Navigating self-driving labs in chemical and material sciences95Active learning streamlines development of high performance catalysts for higher alcohol synthesis75Targeted materials discovery using Bayesian algorithm execution48Design of multicomponent thermosetting polymers with enhanced tensile properties through active learning21Unsupervised learning-aided extrapolation for accelerated design of superalloys21Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories42Self-Driving Laboratories for Chemistry and Materials Science674Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward38Autonomous Optimization of Air‐Processed Perovskite Solar Cell in a Multidimensional Parameter Space23Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty21Accelerating CALPHAD-based phase diagram predictions in complex alloys using universal machine learning potentials: Opportunities and challenges66Automating the practice of science: Opportunities, challenges, and implications47Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications19Precision‐Guided Stealth Missiles in Biomedicine: Biological Carrier‐Mediated Nanomedicine Hitchhiking Strategy28Leveraging generative models with periodicity-aware, invertible and invariant representations for crystalline materials design21High entropy powering green energy: hydrogen, batteries, electronics, and catalysis23Phase-Change Memory for In-Memory Computing70Machine Learning Assisted Material Discovery: A Small Data Approach31Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis24Artificial Intelligence‐Driven Approaches in Semiconductor Research44Recent advances in machine learning applications for MXene materials: Design, synthesis, characterization, and commercialization for energy and environmental applications22Materials design with target-oriented Bayesian optimization40Real-time experiment-theory closed-loop interaction for autonomous materials science29Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives53Closed-loop inverse design of high entropy alloys using symbolic regression-oriented optimization24Artificial intelligence-assisted next-generation biomaterials: From design and preparation to medical applications29Artificial Intelligence for Materials Discovery, Development, and Optimization171Steering towards safe self-driving laboratories41AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31High-throughput platforms for machine learning-guided lipid nanoparticle design33Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50AI-driven design of powder-based nanomaterials for smart textiles: from data intelligence to system integration27Machine learning pipelines for the design of solid-state electrolytes30Roadmap for transforming heterogeneous catalysis with artificial intelligence27

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