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RESEARCH PAPER · Matter · 2021

A data fusion approach to optimize compositional stability of halide perovskites

Shijing Sun, Armi Tiihonen, Felipe Oviedo, Zhe Liu, Janak Thapa, Yicheng Zhao, Noor Titan Putri Hartono, Anuj Goyal, Thomas Heumueller, Clio Batali, Alex Encinas, Jason J. Yoo, Ruipeng Li, Zekun Ren, Ian Marius Peters, Christoph Josef Brabec, Moungi G. Bawendi, Vladan Stevanović, John Sheridan Fisher, Tonio Buonassisi

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A data fusion approach to optimize compositional stability of halide perovskitesOriginalMachine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing7Toward autonomous design and synthesis of novel inorganic materials159In data science we trust: Machine learning for stable halide perovskites5Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop0Autonomous Optimization of Fluid Systems at Varying Length Scales.1Recent Progress on Formamidinium‐Dominated Perovskite Photovoltaics90Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants0Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants0Becoming Good at AI for Good13Autonomous experimentation systems for materials development: A community perspective398Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants1Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants0Machine Learning in Materials Science24Metal Oxide-Induced Instability and Its Mitigation in Halide Perovskite Solar Cells43Lignin Biorefinery Optimization Through Machine Learning5Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries58Applied Machine Learning for Developing Next‐Generation Functional Materials72Machine learning for high-throughput experimental exploration of metal halide perovskites117High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains225A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics32Tailoring capping-layer composition for improved stability of mixed-halide perovskites10Machine Learning Guided Dopant Selection for Metal Oxide‐Based Photoelectrochemical Water Splitting: The Case Study of Fe2O3 and CuO73Mixing Matters: Nanoscale Heterogeneity and Stability in Metal Halide Perovskite Solar Cells53A self-driving laboratory designed to accelerate the discovery of adhesive materials57Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence62Bayesian optimization with known experimental and design constraints for chemistry applications89Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning4Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning32A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation54Enhanced proton irradiation resistance in Cs-doped CH3NH3PbI3 films and solar cells15Active meta-learning for predicting and selecting perovskite crystallization experiments24Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy26Relative straggle of sputtering parameters as a potential measure of power conversion efficiency of Perovskite materials0Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries72Autonomous Nanocrystal Doping by Self‐Driving Fluidic Micro‐Processors20Water-Accelerated Photooxidation of CH3NH3PbI3 Perovskite90Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing217Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning26The case for data science in experimental chemistry: examples and recommendations83Active learning-guided exploration of parameter space of air plasmas to enhance the energy efficiency of NO x production24Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up35A review on theoretical studies of structural and optoelectronic properties of FA ‐based perovskite materials with a focus on FAPbI 343High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective16Interpretable and Explainable Machine Learning for Materials Science and Chemistry421Machine Learning Optimization of Lignin Properties in Green Biorefineries63Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization1Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives53Recent progress and future prospects on halide perovskite nanocrystals for optoelectronics and beyond48Machine learning for a sustainable energy future517Bayesian Optimization of photonic curing process for flexible perovskite photovoltaic devices40Cage Molecules Stabilize Lead Halide Perovskite Thin Films17Networks and interfaces as catalysts for polymer materials innovation10Compositional engineering of perovskites with machine learning18Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118The Spring of Processing Chemistry in Perovskite Solar Cells–Bayesian Optimization16An Integrated Method of Bayesian Optimization and D-Optimal Design for Chemical Experiment Optimization9A scientific machine learning framework to understand flash graphene synthesis24An open-source environmental chamber for materials-stability testing using an optical proxy13Bayesian optimisation for efficient material discovery: a mini review49Compositional Space Is Not Uniform: Density Gradients in Data-Driven Screening0Does it mix? Insights and attempts to predict the formability of single phase mixed A-cation lead iodide perovskites8Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization33Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations41The Curse of Optimality: When Perfect Optimization Undermines Scientific Understanding0Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Doping Strategies for Promising Organic–Inorganic Halide Perovskites24Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137The rise of self-driving labs in chemical and materials sciences570Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification85Monitoring the stability and degradation mechanisms of perovskite solar cells by in situ and operando characterization48NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science0Vision-driven Autocharacterization of Perovskite Semiconductors0Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science8Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)34Transforming characterization data into information in the case of perovskite solar cells8Closed‐Loop Error‐Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials14NIMS-OS: an automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science23Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys31Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset113Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Accelerated Screening of Ternary Chalcogenides for Potential Photovoltaic Applications29In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Machine learning in process systems engineering: Challenges and opportunities136An autonomous laboratory for the accelerated synthesis of inorganic materials1000High-Throughput Multi-Scale Experiments to Probe the Stability of Metal Halide Perovskites11Advancing high-throughput combinatorial aging studies of hybrid perovskite thin films via precise automated characterization methods and machine learning assisted analysis14Autonomous laboratories for accelerated materials discovery: a community survey and practical insights33Failure Modes in Materials AI Explanations: A Conceptual Catalog and Prevention Principles0Interpreting Materials Data with Artificial Intelligence: From Prediction to Scientific Understanding0On the prospects of high-entropy organic A-site halide perovskites10PANDA: a self-driving lab for studying electrodeposited polymer films27Physiochemical machine learning models predict operational lifetimes of CH 3 NH 3 PbI 3 perovskite solar cells25Progress on Large-area Organic-inorganic Hybrid Perovskite Films and Its Photovoltaic Application2Race to the bottom: Bayesian optimisation for chemical problems76Shining light on hybrid perovskites for photoelectrochemical solar to fuel conversion5Thermal degradation in methylammonium–formamidinium–guanidinium lead iodide perovskites12Accelerating Photostability Evaluation of Perovskite Films through Intelligent Spectral Learning-Based Early Diagnosis20Integration of Multijunction Absorbers and Catalysts for Efficient Solar‐Driven Artificial Leaf Structures: A Physical and Materials Science Perspective25Data science accelerates energy device development1Data science accelerates energy device development1Mixed-Halide Perovskite Alloys CsPb(I1–xBrx)3 and CsPb(Br1–xClx)3: New Insight of Configurational Entropy Effect from First-Principles and Phase Diagrams11Using scalable computer vision to automate high-throughput semiconductor characterization20Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review32Structural Disorder by Octahedral Tilting in Inorganic Halide Perovskites: New Insight with Bayesian Optimization11Digital manufacturing of perovskite materials and solar cells12Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization8Bayesian optimization and prediction of the durability of triple-halide perovskite thin films under light and heat stressors17Dopant-induced interactions in spiro-OMeTAD: Advancing hole transport for perovskite solar cells49Développement d'une méthodologie de criblage à haut débit pour les conducteurs ioniques solides du lithium0An AI-accelerated pathway for reproducible and stable halide perovskites17Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation23Atlas: a brain for self-driving laboratories50Autonomous laboratories in China: an embodied intelligence-driven platform to accelerate chemical discovery20Bimolecular amine vapor passivation for efficient perovskite solar cells based on blade-coated FAPbI 321Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36From a Binary Feature Matrix to Correlation Analysis: A Dual-Paradigm Classification of Global Robotics Research Objectives1Performance optimization of perovskite solar cells with an automated spin coating system and artificial intelligence technologies17Strategies for performance and stability advancement in multicomponent perovskite photovoltaics19Roadmap on metal-halide perovskite semiconductors and devices17How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?9Structured sampling strategies in Bayesian optimization: evaluation in mathematical and real-world scenarios12Computational design of additively manufacturable, cost-effective, high-strength aluminum alloys exploiting rapid solidification6Data-efficient optimization of thermally-activated polymer actuators through machine learning11Efficient dataset generation for machine learning halide perovskite alloys7Unlocking a self-driving research workflow for perovskite photovoltaics12Optimization of energy grid efficiency with machine learning: A comprehensive review of challenges and opportunities9Real-time experiment-theory closed-loop interaction for autonomous materials science29AI‐Driven Microphysiological Systems for Advancing Nanoparticle Therapeutics7Data‐Driven Molecular Encoding for Efficient Screening of Organic Additives in Perovskite Solar Cells12The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers6Limitations of theory-informed machine learning algorithms for the prediction and exploration of molecular properties: Solvation free energy as a case study3Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50Multiscale Coupling Between Macroscopic Mechanics and Atomic Assembly (MM–AA) of Soft‐Lattice Halide Perovskites6Machine Learning Unlocks New Directions in Halide Perovskite Research6Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells7Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing3Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications0Bayesian Optimisation Against Climate Change: Applications and Benchmarks1Closed-loop robotic manipulation of transparent substrates for self-driving laboratories using deep learning micro-error correction0Foundation Models for Process Parameters Optimization: Evaluating TabPFN for Bayesian Optimization in HMLV Manufacturing0Machine Learning and First-Principles Investigation of Lead-Free Wide-Bandgap Double Perovskites for Ultraviolet Optoelectronic Applications0Machine Learning and First-Principles Investigation of Lead-Free Wide-Bandgap Double Perovskites for Ultraviolet Optoelectronic Applications0Adaptive defect passivation of perovskite solar cells with 2-mercaptoethylammonium chloride advancing over 2000 h photothermal stability1Recent advances in data‐driven and artificial intelligence‐integrated perovskite solar cells: From design to self‐driving laboratories7Multi-variable batch Bayesian optimization in materials research: Synthetic data analysis of noise sensitivity and problem landscape effects2Stepwise multi-target optimization of two-step deposition for wide-bandgap perovskite solar cells0A Comprehensive Review of the Computational Approaches for Structure–Property Prediction of High Entropy Materials0Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows5

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  48. Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization

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  49. Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives

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