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RESEARCH PAPER · RSC Advances · 2021

Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learning

Yaoyao Li, Yao Lu, Xiaomin Huo, Dong Wei, Juan Meng, Jie Dong, Bo Qiao, Suling Zhao, Zheng Xu, Dandan Song

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Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learningOriginalThe Neoteric technical advances for improving Physical and Optical characteristics of Perovskite solar cells - A review0Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning47Predicting the band gap of binary compounds from machine-learning regression methods4Predicting the photon energy of quasi-2D lead halide perovskites from the precursor composition through machine learning9Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells51Key Factors Governing the External Quantum Efficiency of Thermally Activated Delayed Fluorescence Organic Light-Emitting Devices: Evidence from Machine Learning38Future Research Directions in Perovskite Solar Cells: Exquisite Photon Management and Thermodynamic Phase Stability40Bandgap graded perovskite solar cell for above 30% efficiency32Optical Simulations in Perovskite Devices: A Critical Analysis12Applications of machine learning in perovskite materials80Predicting the device performance of the perovskite solar cells from the experimental parameters through machine learning of existing experimental results82All-in-One Electric Double Layer Supercapacitors Based on CH3NH3PbI3 Perovskite Electrodes24First-principles study on the role of anion in the physical properties of lead-free halide double perovskites3Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells21Bandgap Assessment of Compositional Variation for Uncovering High‐Efficiency Improved Stable All‐Inorganic Lead‐Free Perovskite Solar Cells40Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Machine learning enables intelligent screening of interface materials towards minimizing voltage losses for p-i-n type perovskite solar cells50Machine learning for perovskite solar cell design58Machine learning-assisted design of wide bandgap perovskite materials for high-efficiency indoor photovoltaic applications27The role of machine learning in perovskite solar cell research42Emerging opportunities for hybrid perovskite solar cells using machine learning8Auxiliary guidance manufacture and revealing potential mechanism of perovskite solar cell using machine learning18Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Synergistic passivation of defects for inorganic perovskite solar cells with reduced open-circuit voltage deficiency3Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Data‐Driven Fine Element Tuning of Halide Double Perovskite for Enhanced Photoluminescence6Bandgap Prediction of Hybrid Organic–Inorganic Perovskite Solar Cell Using Machine Learning15Functionalized MXene/Halide Perovskite Heterojunctions for Perovskite Solar Cells Stable Under Real Outdoor Conditions61ADDRESSING THE DRAWBACKS OF PEROVSKITE PHOTOVOLTAICS TO IMPROVE SOLAR ENERGY TECHNOLOGIES: A REVIEW2Advancing perovskite solar cell performance: Enhanced efficiency and stability through superimposed PbS QDs11Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques17Green construction of fused heterocycles & synthesis of carbo-benzenic phosphine derivatives0Effective Encapsulation and Surface Treatment for Damp‐Heat Stable Triple Cation Perovskite Solar Cells5Identifying Molecular Structure–Energy Level Quantitative Relationship of Thermally Activated Delayed Fluorescence Materials Using Machine Learning11Temperature Dependence of Photoinduced Phase Segregation in Bromide-Rich Mixed Halide Perovskites13Carrier Trapping Deactivation by Halide Alloying in Formamidinium‐Based Lead Iodide Perovskites5Data‐Driven Approach to Accelerate the Design of Halide Perovskite for Photovoltaic Application Using Electronic Properties as Descriptors6Predicting the band gap of lead-free inorganic double perovskites using modified parallel residual network2Perovskite Solar Cells: A Review of the Latest Advances in Materials, Fabrication Techniques, and Stability Enhancement Strategies247Strain‐Induced α‐Phase Stabilization for Low Dark Current FAPI‐Based Photodetectors16Sensing Utilities of Cesium Lead Halide Perovskites and Composites: A Comprehensive Review36Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning8Machine‐Learning‐Assisted Discovery of Mechanosynthesized Lead‐Free Metal Halide Perovskites for the Oxidative Photocatalytic Cleavage of Alkenes22Machine learning guided efficiency improvement for Sn‐based perovskite solar cells with efficiency exceeding 20%43Collaborative Fabrication of High-Quality Perovskite Films for Efficient Solar Modules through Solvent Engineering and Vacuum Flash System9Performance prediction and analysis of perovskite solar cells using machine learning10Overview of the Recent Findings in the Perovskite-Type Structures Used for Solar Cells and Hydrogen Storage25Recent progress in the development of metal halide perovskite electronics for sensing applications15Thermally evaporated perovskite light-emitting diodes for wide-color-gamut displays in AR/VR devices17Overcoming the Open-Circuit Voltage Losses in Narrow Bandgap Perovskites for All-Perovskite Tandem Solar Cells12Machine 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 integration41Predictive Modeling for Bandgap Tuning in Perovskite Solar Cells Using Machine Learning0A comprehensive optimization of highly efficient MA-Free wide-bandgap perovskites for 4-T Perovskite/Silicon tandem solar cells24Förster Resonance Energy Transfer in Metal Halide Perovskite: Current Status and Future Prospects12A reliable, colloidal synthesis method of the orthorhombic chalcogenide perovskite, BaZrS 3 , and related ABS 3 nanomaterials (A = Sr, Ba; B = Ti, Zr, Hf): a step forward for earth-abundant, functional materials20Impact of structural advancements interface engineering operational stability and commercial viability of perovskite/silicon tandem solar cells28Advancing all-perovskite two-terminal tandem solar cells: optimization of wide- and narrow-bandgap perovskites and interconnecting layers53Chloride-improved crystallization in sequentially vacuum-deposited perovskites for p–i–n perovskite solar cells12Comparative analysis of perovskites and other semiconductors0Spectral accessibility tuning through perovskite quantum dots for up-/down-converting photovoltaic applications: a review3Theoretical advances and future perspectives of all-inorganic germanium-based perovskites9A Review of Perovskite-Based Solar Cells over the Last Decade: The Evolution of the Hole Transport Layer and the Use of WO3 as an Electron Transport Layer19Additive engineering of nonlinear optical properties of all-inorganic halide perovskites2Quantitative Analysis of Perovskite Morphologies Employing Deep Learning Framework Enables Accurate Solar Cell Performance Prediction11Band gap tuning in inorganic chlorine-based halide perovskites via solid-state synthesis at room temperature3Optimizing novel device configurations for perovskite solar cells: Enhancing stability and efficiency through machine learning on a large dataset18Optimization and Scalability of Polymer‐Modified PSCs Investigated by Machine Learning4Enhanced Lattice Coherences and Improved Structural Stability in Quadruple A‐Site Substituted Lead Bromide Perovskites8Graph Neural Network Based Macroscale AI Model for Perovskite Solar Cell Power Conversion Efficiency Prediction0Outlook and Future Directions for Indoor Photovoltaics: Modern Societal Needs and Challenges3Synthesis of doped ZIF-8@ZIF-67 hybrid nanocomposites: a strategy for enhancing supercapacitor performance for energy storage device8Perovskite solar cells empowered by machine learning16Logistic regression exploration of host–dopant compatibility in MR-TADF-based OLEDs0Predicting high-performance perovskite solar cells using AI-based machine learning models13Cubic Cesium Lead Bromide Stabilized by Ethylammonium Incorporation3Lead-free halide perovskite memristors for scalable crossbar arrays15Ammonium bromide as a saturable absorbing device for ultrafast photonics0Data-Driven Perovskite Design via High-Throughput Simulation and Machine Learning10From Data‐Driven to Intelligence‐Driven: Innovating Research Paradigms of Perovskite Solar Cells4Halide Perovskites for Neuromorphic Sensing and Computing12Simulation‐Driven Bandgap Optimization of Absorber Layer Cs x (FA 0.88 MA 0.12 ) 1− x Pb(I 1− y Br y ) 3 for 25.8% Efficient n‐i‐p Solar Cells Under Outdoor Setup4Algorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells3Accelerating perovskite solar cell design using machine learning: a comparative study on Pb and Sn compositions0From first principles to machine learning: accelerating the design and discovery of hybrid organic–inorganic perovskites0Machine learning insights into band gap properties in halide-based perovskites0Perovskite solar cells towards industrialization: overcoming challenges with data-driven strategies8Resolving ionic spectra of lead-halide perovskites to the nanometer0Vacuum-mediated crystallization stabilizes corner-sharing CsPbBrI perovskite without thermal annealing0Data-driven AI approaches for screening high-efficiency, stable, and lead-free perovskite photovoltaic materials: a review2All‐Optical Modulated Bidirectional Responsive Synaptic Devices: Materials, Mechanisms, and Applications7Machine Learning Optimization of Perovskite Absorber Layer Compositions for Enhancing Perovskite Solar Cell Efficiency1Wafer‐Scale Room‐Temperature Processing of Lead‐Free Perovskites for Optoelectronic Applications5Photoinduced Phase Segregation in Mixed Halide Perovskites: Mechanisms, Suppression Strategies, and Device Performance Optimization3Thermally Stable Halide-Tuned CsPbX 3 /CdTe Four-Terminal Tandem Solar Cells with 25% Efficiency1PerovLearn: An Interpretable Ensemble Machine Learning Framework for Bandgap Prediction in Hybrid Halide Perovskite Solar Cell Materials4Preparation of high quality CsPbI3 perovskite solar cells via CsF buried crystals1Flexoelectricity in Photoconversion: Fundamentals, Materials, and Outlooks2Curated Data Set, Structure–Property Relationships, and Machine Learning Prediction of Band Gaps for One-Dimensional Bismuth(III) and Antimony(III) Halides0A unified machine learning framework for band gap prediction in lead-free ABX₃ and double perovskites0Critical analysis of recent advancements in incorporating Cs in hybrid perovskite solar cell to boost its stability and efficiency0Stoichiometric MACl/PbCl2 Additive Engineering for Improved Reproducibility and Carrier Lifetime in FA/Cs Mixed-Halide Perovskite Solar Cells0Evaluating the radiation tolerance and halide-dependent stability of Cs 0.15 FA 0.85 Pb(I/Br/Cl)3 thin films for optoelectronic applications0Strain-engineered electronic and optoelectronic attributes of FAGeCl3 perovskite under biaxial strain and spin-orbit coupling: A DFT framework0Dual-phosphonic-acid-anchored self-assembled monolayers enhance bottom-electrode interfaces in perovskite memristors0Advancements and Challenges in Perovskite Solar Cells: A Comprehensive Review0

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