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RESEARCH PAPER · Advanced Intelligent Discovery · 2025

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph Josef Brabec

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9 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
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42 direct citing works

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A ReviewOriginalScientific Blind Spots Introduced by Feature Engineering in Materials Informatics0The Role of Mutual Information Estimator Choice in Feature Selection: An Empirical Study on mRMR21Enhancing RbSnBr3 perovskite solar cells performances: machine learning-driven optimization of active layers and electron transport materials using SCAPS-1D4Empowering Solar Technologies: A Review of Machine Learning-Driven Photovoltaic Systems1Integrated simulation and machine learning framework for high-performance lead-free RbGeI3 perovskite solar cells with WS2/CuI transport layers4Analysing the State of Lead Halide Perovskites Through 38,000 Photovoltaic Device Records0Conductivity Spectroscopy for Investigation and Discovery of Photovoltaic Materials4Computational and Data‐Driven Exploration of Structural, Optical, and Photocatalytic Behaviors in Lead‐Free Cs 2 GeX 6 (X = F, Cl, Br, and I) Double Halide Perovskites0Computational and ML methods in MOF based supercapacitors - from mechanistic understanding to future materials design0

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9 direct citing papers saved.

  1. Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Fatima Zahra Amrani, Youssef Benali, Samira El-Haddad

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  2. The Role of Mutual Information Estimator Choice in Feature Selection: An Empirical Study on mRMR

    Information · 2025-08-25

    Nikolaos Papaioannou, Georgios Myllis, Alkiviadis Tsimpiris, Vasiliki G. Vrana

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  3. Enhancing RbSnBr3 perovskite solar cells performances: machine learning-driven optimization of active layers and electron transport materials using SCAPS-1D

    The European Physical Journal Plus · 2025-10-16

    Md. Shamim Reza, Mahbuba Moumita, Avijit Ghosh, Md Aliahsan Bappy, Abdur Rahman, Md. Selim Reza, Kutub Uddin Apu, Aijaz Rasool Chaudhry, Md Al Imran, Khorshed Alam

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  4. Empowering Solar Technologies: A Review of Machine Learning-Driven Photovoltaic Systems

    IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · 2025-11-01

    Deepika Maan, Ashish Kumar, Sushil Kumar Jain, Ajay Kumar, Lokesh Sharma

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  5. Integrated simulation and machine learning framework for high-performance lead-free RbGeI3 perovskite solar cells with WS2/CuI transport layers

    Journal of Computational Electronics · 2026-01-05

    Umar Farooq Ali, Qasim Ali, Asif Ali, Hussain Noor, Usama Sohail, Sabah Haider

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  6. Analysing the State of Lead Halide Perovskites Through 38,000 Photovoltaic Device Records

    Energy Technology · 2026-04-01

    Muthuganesh M, Hasantheeka Senthilkumar, Sujit Kumar

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  7. Conductivity Spectroscopy for Investigation and Discovery of Photovoltaic Materials

    Chemical Reviews · 2026-05-19

    Obadiah G. Reid, Bryon W. Larson

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  8. Computational and Data‐Driven Exploration of Structural, Optical, and Photocatalytic Behaviors in Lead‐Free Cs 2 GeX 6 (X = F, Cl, Br, and I) Double Halide Perovskites

    ChemistrySelect · 2026-08-01

    Sudip Dey Dipta, Tanwi Chakraborty, Anoy Chakraborty, Uddhob Banik, Md Rasidul Islam, Md Masud Rana

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  9. Computational and ML methods in MOF based supercapacitors - from mechanistic understanding to future materials design

    Journal of Materials Science Materials Theory · 2026-08-29

    Achal Siddharth Fulmali, Himanshu Sekhar Panda

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