Reconstructing Solar Materials Innovation Through Data Science: A Review of Opportunities and Structural Bottlenecks

This review highlights the transformative role of data science in accelerating the design and discovery of advanced solar cell materials. Moving beyond traditional trial‐and‐error and computationally intensive quantum methods, data‐driven approaches offer a powerful alternative for rapid and cost‐effective materials screening. We critically examine key components of the data science workflow, including data collection from literature and databases, data quality considerations, feature engineering through molecular descriptors, and the application of machine learning, data mining, and quantitative structure–property relationship (QSPR) modeling. The review covers recent advancements in organic solar cells, dye‐sensitized solar cells, and perovskite solar cells, emphasizing successful case studies of predictive modeling and high‐throughput screening. Furthermore, we identify major challenges such as limited data availability, descriptor limitations, lack of experimental validation, and the need for user‐friendly tools. Finally, strategic recommendations are proposed to overcome these bottlenecks and enable more reliable, scalable, and practical data‐driven materials innovation.