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RESEARCH PAPER · Journal of Alloys and Compounds · 2023

The role of machine learning in perovskite solar cell research

Chen Chen, Ayman Maqsood, T. Jesper Jacobsson

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The role of machine learning in perovskite solar cell researchOriginalTargeting high performance of perovskite solar cells by combining electronic, manufacturing and environmental features in machine learning techniques21Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization3Application of large datasets to assess trends in the stability of perovskite photovoltaics through machine learning20Towards scalability for metal halide perovskites photovoltaics1Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning16Bayesian Optimization with Experience for Fast Development of Monolithic Tandem Solar Cells: Simulation Case Study5The Future of Material Scientists in an Age of Artificial Intelligence76Optimizing the performance of vapor-deposited perovskite solar cells through advanced predictive modeling21Exploring the influence of crystallization fouling on microscale heat exchangers through machine learning analysis12The use of ChatGPT to generate experimentally testable hypotheses for improving the surface passivation of perovskite solar cells16Performance prediction and analysis of perovskite solar cells using machine learning10Predictive Modeling and SHAP (SHapley Additive ExPlanations) Analysis for Enhancing Natural Dye‐Sensitized Solar Cell Performance15A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization8Predicting and analyzing stability in perovskite solar cells: Insights from machine learning models and SHAP analysis19Accelerating Perovskite Solar Cells Development with Machine Learning: Optimizing Efficiency Through Modeling and Shap Analysis0Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36Quantum Circuit Learning for Predicting Nature of Band Gap of Perovskite Oxides3Quantum machine learning for ABO3 perovskite structure prediction18Interpretable machine learning insights of power conversion efficiency for hybrid perovskites solar cells15Predicting lattice constant in ABX3 perovskite via quantum machine learning7Unlocking a self-driving research workflow for perovskite photovoltaics12Multi-Model Deep Learning Framework for Defect Detection in Mixed-Dimensionality FAPbI₃ Perovskite Films0Comparative convolutional neural networks for perovskite solar cell PCE predictions6Solvent‐Free Fabrication Methods of Metal Halide Perovskites19Advances in perovskite indoor photovoltaics for intelligent Internet of Things7An innovative machine learning based approach for predicting the efficiency of a chalcogenide perovskite semiconductor photovoltaic cell4Towards an interoperable perovskite description or how to keep track of 300 perovskite ions6Predicting solar cell efficiencies using historical data from a manufacturing process0Enhanced quantum support vector regression with quantum kernels and virtual sampling for ABX3 perovskite formation energy2Educational and Training Tools for AI in Polymer Science0Data-driven AI approaches for screening high-efficiency, stable, and lead-free perovskite photovoltaic materials: a review2Density Estimation Based on Mixtures of Gaussians for Perovskite Solar Cells Modeling0Predicting power conversion efficiency of dyes in dye-sensitised solar cells0Research progress on the application of machine learning in perovskite solar cells0Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows5Predicting photovoltaic output parameters of perovskite solar cells using explainable machine learning with physics-informed and simulation-derived descriptors4Optical Density of Perovskite Films as a Novel Parameter in Statistical Modeling for Perovskite Solar Cells1Optimizing the performance of CH3NH3SnBr3–based PSCs with SCAPS-1D and machine learning techniques1Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing3Machine learning-assisted first-principles investigation of structural, electronic, and photovoltaic properties of perovskite materials0From first-principles to device performance: a computational perspective on perovskite solar cell design0

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  2. Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization

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  3. Application of large datasets to assess trends in the stability of perovskite photovoltaics through machine learning

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  4. Towards scalability for metal halide perovskites photovoltaics

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  5. Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning

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  7. The Future of Material Scientists in an Age of Artificial Intelligence

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  8. Optimizing the performance of vapor-deposited perovskite solar cells through advanced predictive modeling

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  9. Exploring the influence of crystallization fouling on microscale heat exchangers through machine learning analysis

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  10. The use of ChatGPT to generate experimentally testable hypotheses for improving the surface passivation of perovskite solar cells

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  12. Predictive Modeling and SHAP (SHapley Additive ExPlanations) Analysis for Enhancing Natural Dye‐Sensitized Solar Cell Performance

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  13. A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization

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    Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, H.-T. Su, Qianliang Xiao, Shi-Yin Gong

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  14. Predicting and analyzing stability in perovskite solar cells: Insights from machine learning models and SHAP analysis

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  15. Accelerating Perovskite Solar Cells Development with Machine Learning: Optimizing Efficiency Through Modeling and Shap Analysis

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  16. Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication

    Energy & Environmental Science · 2025-01-01

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  17. Quantum Circuit Learning for Predicting Nature of Band Gap of Perovskite Oxides

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  18. Quantum machine learning for ABO3 perovskite structure prediction

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  19. Interpretable machine learning insights of power conversion efficiency for hybrid perovskites solar cells

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  20. Predicting lattice constant in ABX3 perovskite via quantum machine learning

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

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  22. Multi-Model Deep Learning Framework for Defect Detection in Mixed-Dimensionality FAPbI₃ Perovskite Films

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  23. Comparative convolutional neural networks for perovskite solar cell PCE predictions

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  27. Towards an interoperable perovskite description or how to keep track of 300 perovskite ions

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  37. Optical Density of Perovskite Films as a Novel Parameter in Statistical Modeling for Perovskite Solar Cells

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