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RESEARCH PAPER · Matter · 2020

Machine-Learning-Accelerated Perovskite Crystallization

Jeffrey Kirman, Andrew K. Johnston, D.A. Kuntz, Mikhail Askerka, Yuan Jun Gao, Petar Todorović́, Dongxin Ma, Gilbert G. Privé, Edward Hartley Sargent

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Machine-Learning-Accelerated Perovskite CrystallizationOriginalNavigating the design space of inorganic materials synthesis using statistical methods and machine learning43Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot323Robot-Accelerated Perovskite Investigation and Discovery187Interface Engineering Driven Stabilization of Halide Perovskites against Moisture, Heat, and Light for Optoelectronic Applications115Machine learning for halide perovskite materials130Flow Synthesis of Metal Halide Perovskite Quantum Dots: From Rapid Parameter Space Mapping to AI-Guided Modular Manufacturing84Quantitative predictions of photo-emission dynamics in metal halide perovskites via machine learning3Pressing challenges of halide perovskite thin film growth67Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine189Low‐Dimensional Metal Halide Perovskite Photodetectors574Accelerated design of promising mixed lead-free double halide organic–inorganic perovskites for photovoltaics using machine learning44High throughput screening of novel tribromide perovskite materials for high-photovoltage solar cells19Machine Learning Accelerated Insights of Perovskite Materials3Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design62A data fusion approach to optimize compositional stability of halide perovskites158Bimetallic-Derived Catalysts and Their Application in Simultaneous Upgrading of CO2 and Ethane59Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions72A robust low data solution: Dimension prediction of semiconductor nanorods13Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion19Nanoparticle synthesis assisted by machine learning545Autonomous experimentation systems for materials development: A community perspective398Photocatalytic Conversion of Methane: Recent Advancements and Prospects251Photocatalytic Conversion of Methane: Recent Advancements and Prospects95Is machine learning redefining the perovskite solar cells?59Machine Learning Roadmap for Perovskite Photovoltaics85Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries58Applied Machine Learning for Developing Next‐Generation Functional Materials72Roadmap on organic–inorganic hybrid perovskite semiconductors and devices201Recent advances in autonomous synthesis of materials33Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization77High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics32The evolution and future of metal halide perovskite-based optoelectronic devices83From Stochastic Self‐Assembly of Nanoparticles to Nanostructured (Photo)Electrocatalysts for Renewable Power‐to‐X Applications via Scalable Flame Synthesis29Discovery of Lead‐Free Perovskites for High‐Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities83Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence62Development and Prospects of Halide Perovskite Single Crystal Films19Shedding light on the energy applications of emerging 2D hybrid organic-inorganic halide perovskites56Active meta-learning for predicting and selecting perovskite crystallization experiments24Quantitative Predictions of Moisture-Driven Photoemission Dynamics in Metal Halide Perovskites via Machine Learning30Accelerated Design of High-Efficiency Lead-Free Tin Perovskite Solar Cells via Machine Learning27Optical Characterization and Prediction with Neural Network Modeling of Various Stoichiometries of Perovskite Materials Using a Hyperregression Method9Text Mining for Energy Materials3Vision for energy material design: A roadmap for integrated data-driven modeling27Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up35High-Throughput Evaluation of Emission and Structure in Reduced-Dimensional Perovskites10High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective16Review on Perovskite-Type Compound Using Machine Learning5Spatiotemporal Route to Understanding Metal Halide Perovskitoid Crystallization7Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction26The effects of organic cation rotation in hybrid Organic-Inorganic Perovskites: A critical review48Principled Exploration of Bipyridine and Terpyridine Additives to Promote Methylammonium Lead Iodide Perovskite Crystallization13Shape‐Controlled Solution‐Epitaxial Perovskite Micro‐Crystal Lasers Rivaling Vapor Deposited Ones13Machine‐Learning Modeling for Ultra‐Stable High‐Efficiency Perovskite Solar Cells86Accelerated innovation in developing high-performance metal halide perovskite solar cell using machine learning19Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses11Machine learning for a sustainable energy future517Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Thermal evaporation and hybrid deposition of perovskite solar cells and mini-modules191Accelerated chemical space search using a quantum-inspired cluster expansion approach25Composition Engineering of Perovskite Single Crystals for High‐Performance Optoelectronics115Ab initio studies on perovskites3Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning12Machine learning in energy chemistry: introduction, challenges and perspectives31Perovskite-quantum dot hybrid solar cells: a multi-win strategy for high performance and stability17Recent Advance of Machine Learning in Selecting New Materials7Smart Microfluidics: Synergy of Machine Learning and Microfluidics in the Development of Medical Diagnostics for Chronic and Emerging Infectious Diseases3Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Discovering a formula for the high temperature oxidation behavior of FeCrAlCoNi based high entropy alloys by domain knowledge-guided machine learning41High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap35Accelerated prediction of perovskite material properties with classical machine learning and graph neural network6Perspective on machine learning based investigated materials to realize Stable-Efficient-Toxin free (S-E-T) perovskite solar cells7Methods, progresses, and opportunities of materials informatics72The role of machine learning in perovskite solar cell research42Probing the Low-Frequency Response of Impedance Spectroscopy of Halide Perovskite Single Crystals Using Machine Learning21Photocatalytic Conversion of Methane: Current State of the Art, Challenges, and Future Perspectives98Machine learning for fast development of advanced energy materials23A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites18Emerging opportunities for hybrid perovskite solar cells using machine learning8Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion3Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Methoxy‐Based Passivator 4‐Methoxyphenethylammonium Iodide as Multifunctional Passivator for High‐Efficiency 3D/2D Flexible Perovskite Solar Cells over 23%10Accelerated design of stable halide perovskite heterostructure film in hostile condition via surface modifier5Comprehensive and Accurate Prediction of Band Gap for Lead-Free Double Perovskites through Self-Modified Machine Learning Strategy10Accelerated Discovery of Perovskite-Inspired Materials through Robotized Screening Including Solar Cell Characterization2Methods and Models of Machine Learning in Chemistry and Material Science Using Solute Diffusion Experiment0Multi-Modal Transformer Architectures for Correlating Processing Conditions to Functional Performance in Perovskite Solar Cells0Nature-inspired and computer-aided approaches to enable improved photovoltaic materials, more efficient processing, and novel devices0Role of quantum technology and artificial intelligence for nano-enabled microfluidics3Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory86Surface Patterned Omniphobic Tiles (SPOTs): a versatile platform for scalable liquid handling7Review of computer‐aided methods in fat crystallization studies2Photocatalytic Reactors for the Production of Syngas Through Natural Gas Methane0DLSIA: Deep Learning for Scientific Image Analysis11The Future of Material Scientists in an Age of Artificial Intelligence76Structurally Flexible 2D Spacer for Suppressing the Electron–Phonon Coupling Induced Non-Radiative Decay in Perovskite Solar Cells37Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions5Machine learning-based screening of two-dimensional perovskite organic spacers13Adapting Explainable Machine Learning to Study Mechanical Properties of 2D Hybrid Halide Perovskites16Self-Driving Laboratories for Chemistry and Materials Science674Machine learning for perovskite optoelectronics: a review24Machine learning for screening and predicting the best surface modifiers for a rational optimization of efficient perovskite solar cells9Repeatable Perovskite Solar Cells through Fully Automated Spin-Coating and Quenching21Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices37Machine Learning Approaches in Advancing Perovskite Solar Cells Research16A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration41Machine learning-assisted design of high-performance perovskite photodetectors: a review22Accelerating the design and manufacturing of perovskite solar cells using a one-shot automated machine learning framework10An Intermediate-Aided Perovskite Phase Purification for High-Performance Solar Cells41Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36Indoor light energy harvesting perovskite solar cells: from device physics to AI-driven strategies20Lead-free perovskites for next-generation applications: a comprehensive computational and data-driven review27Machine learning for predicting and improving perovskite properties0Automated nanoarchitectonics for networks filling by Prussian blue single crystals through an intermittentto-flow conversion2Atomistic Origin of Microsecond Carrier Lifetimes at Perovskite Grain Boundaries: Machine Learning-Assisted Nonadiabatic Molecular Dynamics27Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors4Science acceleration and accessibility with self-driving labs83Perovskite Single Crystals for Detection of High‐Energy Ionizing Radiations1Unlocking a self-driving research workflow for perovskite photovoltaics12Progress of Machine Learning in Molecular Crystal Design and Crystallization Development11Artificial Intelligence‐Driven Approaches in Semiconductor Research44Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations1Optimization of energy grid efficiency with machine learning: A comprehensive review of challenges and opportunities9Comparative convolutional neural networks for perovskite solar cell PCE predictions6Machine learning for 2D material–based devices13Perovskite Materials for Next‐Generation Spectral Imaging: From Tunable Optoelectronics to Advanced Device Architectures1Bridging artificial intelligence with photovoltaics7Review: machine learning-driven design and discovery of halide perovskite materials1Interpretable machine learning on a curated dataset identifies chemical descriptors governing 2D perovskite solar cell performance1Material Modification Strategies for High-Performance Perovskite Optoelectronics: From Solar Cells to Photocatalysis4Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites0Additive and interface engineering for narrow-bandgap Pb–Sn perovskite solar cells toward tandem applications0AI-driven design and applications of quantum dots9Advances and prospects of lead-free metal halide perovskites for solar hydrogen fuel generation2Temperature‐Dependent Crystallization in Two‐Step Perovskite Deposition Revealed by In Situ GIWAXS and Machine Learning‐Guided Analysis0Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories1Halide Perovskite Single Crystals: A Promising Semiconductor Platform for Advanced X-ray Detection4Fabrication and comprehensive classification of Perovskite-based solar materials methodologies for sustainable energy applications0Accelerating the design of ABO3‑δ perovskite electrolytes via an interpretable machine learning platform with fused multi‑source descriptors0Perovskite solar cells: Past, present, and future10Engineering stability in metal halide perovskites: From fundamental strategies to high-performance sensing applications0

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  47. High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective

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  48. Review on Perovskite-Type Compound Using Machine Learning

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  49. Spatiotemporal Route to Understanding Metal Halide Perovskitoid Crystallization

    Chemistry of Materials · 2022-06-07

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  50. Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

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