Key Takeaways & Executive Findings
- •• • The framework leverages over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features, enabling a highly generalizable performance prediction model with prediction error comparable to small-scale models, directly addressing the industrial bottleneck of data scarcity and poor model transferability across diverse device architectures. • • Experimental validation on 8 types of PSCs with new feature values yielded an additional PCE improvement of 0.92% to 2.43% (absolute percentage points) over baseline fabricated devices, demonstrating the framework's capacity to generate actionable optimization suggestions for novel material compositions and device stacks. • • The full-process AI framework integrates material selection, fabrication processes, and environmental factors (illumination, temperature, wind speed) into a unified optimization solution, overcoming the device-level oversimplification of prior ML models that failed to capture multi-scale complexity in PSC design. • • The framework's expansion module successfully adapted to new data with sufficient learning and analytical capabilities, proving its universality and ability to keep pace with the rapid development of PSCs, thereby reducing the need for costly and time-consuming traditional experimental optimization or DFT calculations.
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Abstract
The development of high-efficiency perovskite solar cells (PSCs) demands comprehensive control of multi-scale factors influencing device performance. Artificial intelligence (AI), represented by machine learning (ML), has rapidly become a key tool for PSC design and optimization. However, current ML models often oversimplify PSC design at the device level, failing to capture multi-scale complexity. They are constrained by relatively small, specialized datasets, limiting generalizability across diverse architectures and fabrication methods. This work developed a full-process AI framework based on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features. The framework offers significant advantages in sample diversity and feature richness, combining material selection, fabrication processes, and environmental factors to provide accurate, comprehensive optimization solutions. Data diversity and heterogeneity challenges were addressed through feature engineering and model training, yielding a highly generalizable PSC performance prediction model with prediction error comparable to small-scale models. The framework enables precise optimization of specific features for any PSC and provides valuable insights for designing high-performance photovoltaic devices. Experimental validation fabricated 8 types of PSCs with new feature values; the framework searched corresponding optimization suggestions, resulting in an additional improvement of 0.92% to 2.43% in final power conversion efficiency (PCE) of fabricated devices. This demonstrates the framework's universality and sufficient learning and analytical capabilities for new data, adaptable to the rapid development of PSCs.
1. Introduction
Traditional experimental optimization of perovskite solar cells (PSCs) relies on manual operations, resulting in low efficiency and poor reproducibility. Density functional theory (DFT) calculations, while useful for establishing structure-property relationships, demand substantial computational resources and are impractical for high-throughput screening of multi-component device stacks. These limitations have stalled the broader application of both experimental and theoretical tools in the race toward the certified 26.7% single-junction PCE benchmark. Existing machine learning models for PSCs often oversimplify device design at the device level, failing to capture the complexity of multi-scale features, and are constrained by relatively small, specialized datasets that limit generalizability across diverse architectures and fabrication methods.
This work addresses the bottleneck by developing a full-process AI framework trained on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features. The framework combines material selection, fabrication processes, and environmental factors to provide a more accurate and comprehensive optimization solution. Through feature engineering and model training, the authors overcome data diversity and heterogeneity challenges, yielding a highly generalizable performance prediction model with prediction error comparable to small-scale models. The framework enables precise optimization of specific features for any PSC and was experimentally validated on 8 types of PSCs with new feature values, resulting in an additional PCE improvement of 0.92% to 2.43% in fabricated devices.
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Xinyu YE, Wenbin YUAN, Pengfei FU, Xueying YANG, Xiaowen CHU, Yang BAI, Yuanmiao SUN, Hui-Ming CHENG (2025). A Full-Process Artificial Intelligence Framework for Perovskite Solar Cells. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3416-3
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Frequently Asked Questions
What is the quantified improvement in power conversion efficiency (PCE) achieved through the framework's optimization suggestions on newly fabricated PSC devices?
Experimental fabrication of 8 types of PSCs with new feature values, followed by framework-generated optimization suggestions, resulted in an additional PCE improvement of 0.92% to 2.43% (absolute percentage points) in the final fabricated devices. This demonstrates the framework's universality and sufficient learning and analytical capabilities for new data.
How does the framework address the data diversity and heterogeneity challenges that limit generalizability of prior ML models for PSCs?
The framework was trained on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features, combining material selection, fabrication processes, and environmental factors. Through feature engineering and model training, it achieves a highly generalizable performance prediction model with prediction error comparable to small-scale models, overcoming the constraints of relatively small and specialized datasets.
What specific multi-scale factors are integrated into the full-process AI framework for PSC optimization?
The framework integrates material selection (perovskite composition, electron-hole transport layers), fabrication processes (film deposition, encapsulation protocols), and environmental factors (illumination, temperature, wind speed). This comprehensive approach captures the multi-scale complexity that prior device-level oversimplified ML models failed to address.
What is the certified power conversion efficiency (PCE) benchmark for single-junction perovskite solar cells as of the study's context?
The certified PCE of single-junction PSCs has reached 26.7%, as reported by NREL. The framework aims to accelerate the design and optimization of high-performance PSCs to approach and exceed this benchmark through data-driven material screening and device fabrication guidance.
How does the framework's expansion module ensure adaptability to rapidly evolving PSC technologies and new data?
The expansion module was experimentally verified by fabricating 8 types of PSCs with new feature values and using the framework to search for corresponding optimization suggestions, which yielded an additional PCE improvement of 0.92% to 2.43%. This demonstrates sufficient learning and analytical capabilities for new data, enabling the framework to adapt to the rapid development of PSCs.
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