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| Home > Online First > Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review |
| Home > Online First > Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review |
| Review/Journal Article | FZJ-2025-03331 |
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2025
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Please use a persistent id in citations: doi:10.1002/aidi.202500022
Abstract: Developing reliable emerging photovoltaic (e-PV) technologies requires high-throughput material discovery, device design, and processing optimization. However, the effective process of the resulting high-dimensional, multivariate datasets remains a significant challenge. Integrating feature selection methods and machine learning (ML) provides a robust solution to reduce data dimensionality, improve predictive accuracy, and uncover material performance mechanisms. This review summarizes the advancements in synergizing feature selection methods, particularly the maximum relevance minimum redundancy (mRMR) method embedded, with Gaussian process regression (GPR) to advance e-PVs research. It highlights the importance of integrating feature selection with ML and high-throughput experimentation (HTE) frameworks to accelerate material screening, optimize manufacturing processes, and predict stability. Additionally, the review discusses key challenges such as data quality and model scalability and offers promising strategies to address these limitations. This data-driven approach offers a systematic pathway toward the accelerated discovery and optimization of e-PV technologies.
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