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RESEARCH PAPER · The Journal of Physical Chemistry C · 2022

Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance

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Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells PerformanceOriginalPeripheral substituent regulation of bias structured azulene-based hole transport materials for perovskite solar cells7Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Explainable machine learning for predicting the band gaps of ABX3 perovskites44Charge Localization in Acene Crystals fromAb InitioElectronic Structure14Machine learning for perovskite solar cell design5873‐4: The Value and Benefit of Multiscale Material Simulation for OLED R&D: Prediction and Validation of Triplet Emitter Orientation Yielding > 90% In‐plane Transition Dipole Orientation for Ir(ppy) 3 Derivatives0The role of machine learning in perovskite solar cell research42Critical analysis of decision variables for high-throughput experimentation (HTE) with perovskite solar cells15Assessing Performance and Limitations of a Device‐Level Machine Learning Approach for Perovskite Solar Cells with an Application to Hole Transport Materials2Auxiliary guidance manufacture and revealing potential mechanism of perovskite solar cell using machine learning18Morphology, dynamic disorder, and charge transport in an indoloindole-based hole-transporting material from a multi-level theoretical approach7DTBAPred: Improved Prediction Drug-Target Binding Affinity using Machine Learning Approach1Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12A machine learning approach for in silico prediction of the photovoltaic properties of perovskite solar cells based on dopant-free hole-transport materials8Predicting photovoltaic parameters of perovskite solar cells using machine learning9Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells2Perovskite Solar Cells: Challenges Facing Polymeric Hole Selective Materials in p–i–n Configuration18A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration41Enhancing Durability of Organic–Inorganic Hybrid Perovskite Solar Cells in High‐Temperature Environments: Exploring Thermal Stability, Molecular Structures, and AI Applications18Accelerating ionic liquid research in perovskite solar cells through machine learning:Opportunities and challenges5Rational design of 1,3,6-trisubstituted azulene based hole transport materials for efficient and stable planar perovskite solar cells0Unlocking a self-driving research workflow for perovskite photovoltaics12Predicting high-performance perovskite solar cells using AI-based machine learning models13Data-Driven Optimization and Mechanical Assessment of Perovskite Solar Cells via Stacking Ensemble and SHAP Interpretability5Perovskite solar cell efficiency prediction on the basis of structure of organic hole-transporting materials3Algorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells3Inorganic Charge Transport Layers for High‐Performance p‐i‐n Perovskite Solar Cells6Machine Learning Unlocks New Directions in Halide Perovskite Research6Role of AI in Solar Cell Materials Research: A Review0A cost-effective triphenylamine–phenanthroimidazole hybrid hole-transport material for dopant-free perovskite solar cells0A dual-descriptor machine learning framework for evaluating perovskite passivation materials0Research progress on the application of machine learning in perovskite solar cells0Machine learning framework for simultaneous classification of shapes and regression of LSPR wavelengths in various shapes of gold nanoparticles0Performance enhancement of tin-based perovskite solar cells by analysing experimental dataset via machine learning3Machine learning applications in the design, optimization, and manufacturing of perovskite solar cells3Quantum and classical machine learning for perovskite bandgap learning and solar cell performance analysis0Perovskite databases analysis using machine learning and large language models for prediction and reverse engineering0Data‐Driven Design of Self‐Assembled Monolayers for High‐Efficiency Perovskite Solar Cells0

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  2. Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

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  4. Charge Localization in Acene Crystals fromAb InitioElectronic Structure

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  5. Machine learning for perovskite solar cell design

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  8. Critical analysis of decision variables for high-throughput experimentation (HTE) with perovskite solar cells

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  10. Auxiliary guidance manufacture and revealing potential mechanism of perovskite solar cell using machine learning

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  11. Morphology, dynamic disorder, and charge transport in an indoloindole-based hole-transporting material from a multi-level theoretical approach

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  14. A machine learning approach for in silico prediction of the photovoltaic properties of perovskite solar cells based on dopant-free hole-transport materials

    New Journal of Chemistry · 2024-01-01

    Islam M. Abdellah, Ahmed El‐Shafei

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  15. Predicting photovoltaic parameters of perovskite solar cells using machine learning

    Journal of Physics Condensed Matter · 2024-05-28

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  16. Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells

    The Journal of Physical Chemistry C · 2024-07-10

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  17. Perovskite Solar Cells: Challenges Facing Polymeric Hole Selective Materials in p–i–n Configuration

    Advanced Functional Materials · 2024-10-31

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  18. A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration

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  19. Enhancing Durability of Organic–Inorganic Hybrid Perovskite Solar Cells in High‐Temperature Environments: Exploring Thermal Stability, Molecular Structures, and AI Applications

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  20. Accelerating ionic liquid research in perovskite solar cells through machine learning:Opportunities and challenges

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  21. Rational design of 1,3,6-trisubstituted azulene based hole transport materials for efficient and stable planar perovskite solar cells

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  22. Unlocking a self-driving research workflow for perovskite photovoltaics

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  24. Data-Driven Optimization and Mechanical Assessment of Perovskite Solar Cells via Stacking Ensemble and SHAP Interpretability

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  25. Perovskite solar cell efficiency prediction on the basis of structure of organic hole-transporting materials

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  26. Algorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells

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  27. Inorganic Charge Transport Layers for High‐Performance p‐i‐n Perovskite Solar Cells

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  28. Machine Learning Unlocks New Directions in Halide Perovskite Research

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  29. Role of AI in Solar Cell Materials Research: A Review

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  30. A cost-effective triphenylamine–phenanthroimidazole hybrid hole-transport material for dopant-free perovskite solar cells

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  38. Data‐Driven Design of Self‐Assembled Monolayers for High‐Efficiency Perovskite Solar Cells

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