ReviewNanomaterials (Basel, Switzerland)2025
Development of High-Efficiency Perovskite Solar Cells and Their Integration with Machine Learning.
Review in Nanomaterials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Model-Driven Performance Optimization of Lead-Free RbGeIACS omega · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Perovskite solar cells, as a rising star in third-generation photovoltaic technologies, have attracted extensive attention due to their high light absorption, tunable bandgap, and high power conversion efficiency, indicating substantial potential for future applications. Starting from the development history of perovskite solar cells, this review systematically comprehends the technological breakthroughs in the continuous improvement of power conversion efficiency since their invention, outlining the research status and technical bottlenecks. A detailed analysis is provided on the material characteristics and limitations of the lead-based perovskite systems. Critical obstacles towards commercialization are also identified, such as operational instability and the challenges associated with large-scale manufacturing. Finally, the potential role of machine learning in the discovery and design of new perovskite materials is highlighted, and future development directions have been outlined. Special focus is placed on the innovative applications of machine learning in material composition screening, material properties prediction, and process parameter optimization, with the aim of constructing a closed-loop research framework. The review aims to offer valuable insights and references for advancing the performance and industrial applications of perovskite solar cells.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.