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RESEARCH PAPER · Solar Energy · 2021

Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells

Zongmei Guo, Bin Lin

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Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cellsOriginalEfficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning30Investigation of electronic, optical and thermoelectric features of X2ScAgCl6 (X=K, Na) double perovskites for renewable energy applications75Machine-Learning Prediction of the Computed Band Gaps of Double Perovskite Materials2Bandgap model using symbolic regression for environmentally compatible lead-free inorganic double perovskites5An integrated machine learning model for accurate and robust prediction of superconducting critical temperature28A Comprehensive Review of the Application of Machine Learning in Fabrication and Implementation of Photovoltaic Systems80A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B + B 3+ X 615Band gap prediction of perovskite materials based on transfer learning6Comparative Study of Five Classifiers for Predicting Power Conversion Efficiency in Perovskite Solar Cells0Machine learning in energy chemistry: introduction, challenges and perspectives31Research progress of synthesis of high-performance perovskites and its derivatives based on polyhedral distortion0Machine-Learning Prediction of the Computed Band Gaps of Double Perovskite Materials6Designing sulfonated polyimide-based fuel cell polymer electrolyte membranes using machine learning approaches18A Fourier-transformed feature engineering design for predicting ternary perovskite properties by coupling a two-dimensional convolutional neural network with a support vector machine (Conv2D-SVM)7Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Static buckling, vibration analysis and optimization of nanocomposite multilayer perovskite solar cell12Machine-learning-based prediction of cubic perovskite formation energy and magnetism3The role of machine learning in perovskite solar cell research42AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122Machine learning for fast development of advanced energy materials23Perovskites informatics: Studying the impact of thicknesses, doping, and defects on the perovskite solar cell efficiency using a machine learning algorithm34Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Bandgap prediction of ABX3-type perovskites using Broad Learning System12Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Selecting an appropriate machine-learning model for perovskite solar cell datasets58Anion Replacement Effect on the Structural, Magnetoelectronic, and Optical Features of All Inorganic Halide Double Perovskites K2limox6 (X= Br, I) for Spintronic and Optical Devices0Artificial Intelligence for Developing Smart and Sustainable Energy Systems5Data‐Driven Approach to Accelerate the Design of Halide Perovskite for Photovoltaic Application Using Electronic Properties as Descriptors6Machine learning-based prediction of perovskite material photovoltaic conversion efficiency grades0Employing the Interpretable Ensemble Learning Approach to Predict the Bandgaps of the Halide Perovskites12Anion replacement effect on the structural, magnetoelectronic, and optical features of all inorganic halide double perovskites K2LiMoX6 (X= Br, I) for spintronic and optical devices13Applications of Machine Learning in Exploration of the Perovskite Solar Cells0Artificial intelligence driven evolution of perovskite-based solar thermochemical systems for hydrogen production: a narrative review4Combining 2D image and point cloud deep learning to predict wheat above ground biomass18Roles of surfactants in perovskite solar cells10Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices37Solar Cells Based on Pb-Free and Perovskite-Inspired Materials: Futuristic of Perovskite Solar Cells44Machine Learning Models for Efficient Property Prediction of ABX 3 Materials: A High-Throughput Approach22Multi-Properties prediction of perovskite materials using Machine learning and Meta-Heuristic feature selection9Harnessing Nano & AI for Sustainable Energy Application1Machine learning for predicting and improving perovskite properties0Machine Learning‐Driven Band Gap Prediction/Classification and Feature Importance Analysis of Inorganic Perovskites9Simulation and machine learning driven optimization of Rb 2 SnBr 6 -based lead-free perovskite solar cells using diverse ETLs for enhanced photovoltaic performance17机器学习辅助钙钛矿太阳能电池制备工艺参数优化2Design of photovoltaic materials assisted by machine learning and the mechanical tunability under micro-strain17Highly Efficient Screening of Halide Double Perovskite Optoelectronic Materials Based on Machine learning16Double-Perovskite Materials: Possibilities and Reality for a Better Solar Cell Device5Employing interpretable multi-output machine learning to predict stable perovskites in photovoltaics2Machine Learning Assisted Materials Classification to Boost Catalyst Design for Electrochemical Oxidation5SHAP-based interpretable machine learning for predicting the oxide double perovskite band gaps9Perovskite solar cells empowered by machine learning16Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning13Accelerated Multi‐Property Screening of Lead‐Free Halide Double Perovskite via Transfer Learning13Predicting high-performance perovskite solar cells using AI-based machine learning models13Optimizing Inorganic Cs4CuSb2Cl12/Cs2TiI6 Dual-Absorber Solar Cells: SCAPS-1D Simulations and Machine Learning21Material property prediction of perovskite oxides based on machine learning6Design and optimization of Ca3BiI3-based solar cells through a comprehensive analysis of optoelectronic properties and charge transport layers using simulation and ML5Thermodynamic stability prediction of organic–inorganic hybrid perovskites using machine learning2Exploring Lead‐Free Ca 3 BiCl 3 ‐Based Perovskite Solar Cells: A Computational Comparison of Charge Transport Layers With DFT and SCAPS ‐ 1D15Recent advances of theoretical investigation on lead-free metal halide perovskites4Data-driven prediction of structural and mechanical properties of rare-earth pyrochlores0Application of Machine Learning for Performance Analysis and Composition Tuning in Mixed Halide Cs2Ti(Cl1−xBrx)6 Solar Devices2A computational and machine learning-driven investigation of Ca3BiBr3 perovskite solar cells: Fine-tuning the hole transport layer for enhanced performance13A review of morphological studies on CuO nanostructures: comparative analysis of synthesis methods and predictions by machine learning2Hybrid ML model of quantum-embedded Bi-LSTM classifier for the prediction of stable spinel oxides1Machine Learning Unlocks New Directions in Halide Perovskite Research6Accelerating perovskite solar cell design using machine learning: a comparative study on Pb and Sn compositions0Bonding Mechanisms Underpinning Structural and Electronic Properties of Halide Perovskites2Backward Mapping from Device Targets to Chemical Genomes for Interpretable Discovery of Phase-Stable Lead-Free Double Perovskites with DFT-Validated Design Rules0From machine learning based on electronic structure design to strain engineering for improving optoelectronic performance0Octahedral tilting in halide double perovskites: disentangling lone-pair chemistry and geometric effects0Data-driven AI approaches for screening high-efficiency, stable, and lead-free perovskite photovoltaic materials: a review2Predicting perovskite solar cell stability using CatBoost with Bayesian optimization and SHAP interpretability analysis3Machine learning–driven optimization of lead-free Rb 2 GeSnCl 6 double perovskite solar cells for high-efficiency sustainable energy4Multiscale computational and machine learning insights into rare-earth-doped front electrode and La2NiMnO6 double perovskite material for photovoltaic applications and proposed fabrication pathways1PerovLearn: An Interpretable Ensemble Machine Learning Framework for Bandgap Prediction in Hybrid Halide Perovskite Solar Cell Materials4Prediction of Bandgap and Key Feature Analysis of Lead-Free Double Perovskite Oxides Based on Deep Learning1Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges7Machine learning applications in the design, optimization, and manufacturing of perovskite solar cells3Integrating NSGA-II and TOPSIS for Stacking Model Optimization in Pursuit of Halide Double Perovskite Screening0A unified machine learning framework for band gap prediction in lead-free ABX₃ and double perovskites0Machine learning-guided design and strain engineering of lead-free double perovskites for enhanced optoelectronic performance1A hybrid machine learning approach for high throughput screening of thermodynamically stable double perovskites with optimal band gap0Pressure‐Driven Band‐Gap Modulation, Structural, Elastic and Optical Response in Double Perovskite Hf 2 NaLaO 6 : From Ambient to 30 GPa0Backward mapping from target optoelectronic phenotypes to chemical genomes for the discovery of lead-free double perovskites0Formation Energy Prediction and Feature Analysis of Lead-Free Double Perovskites Based on Deep Learning0Machine Learning of Band Gaps and Thermodynamic Stability in A2BB′X6 (X = F, Cl, Br, I) Double Perovskites with Uncertainty-Aware Multi-Objective Screening0Machine learning-guided first-principles screening of halide double perovskites for optoelectronic applications0Atomic-level design and device modeling of lead-free Cs2AgSbCl6 double perovskite solar cells through DFT, SCAPS-1D, and predictive algorithms0Enhancing the performance of numerical simulations and Machine learning implementation of Cs2TiI3Br3-based perovskite solar cells with various ETLs0

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