Key Takeaways & Executive Findings
- •• • The LWEM ML model predicted BMT as an effective passivator; BMT-modified devices achieved a PCE increase from 22.45% to 24.89% under AM 1.5G, a relative improvement of 10.9%, demonstrating the model's predictive power for accelerating material discovery. • • Under 1000 lux LED indoor lighting, BMT-modified devices reached a PCE of 41.31%, highlighting their potential for powering IoT devices in low-light environments, a key market for indoor photovoltaics. • • The 'ion-coordination dual-lock' mechanism suppresses non-radiative recombination and facilitates hole extraction, as evidenced by improved V_OC and FF, which are critical for achieving high efficiency and stability. • • BMT-modified devices exhibited outstanding stability under long-term storage and maximum power point tracking conditions, addressing the commercialization bottleneck of PSCs related to operational lifetime.
Abstract
The commercialization of perovskite solar cells (PSCs) is hindered by stability issues primarily stemming from interfacial defects. This study employed a machine learning (ML) screening approach and constructed a learnable weighted ensemble model (LWEM) to enhance prediction robustness for identifying effective interface passivation materials. The ML model predicted that an imidazolium salt-based interface modifier, 1-benzyl-3-methylimidazolium tetrafluoroborate (BMT), is suitable for planar n-i-p PSCs. Subsequent experimental results demonstrated that BMT provides synergistic passivation via an 'ion-coordination dual-lock' mechanism that significantly suppresses non-radiative recombination, facilitates hole extraction, and improves the quality of the perovskite film. The BMT-modified devices achieve a significant increase in power conversion efficiency (PCE) from 22.45% to 24.89% under AM 1.5G illumination, and attain a high PCE of 41.31% under 1000 lux light emitting diode (LED) indoor lighting. Additionally, the modified devices exhibit outstanding stability under long-term storage and maximum power point tracking conditions. This work provides a strategy for developing high-performance and highly stable PSCs for both indoor and outdoor applications.
1. Introduction
Perovskite solar cells (PSCs) have reached power conversion efficiencies exceeding 26.84%, rivaling silicon photovoltaics, yet their commercial deployment is stalled by interfacial defect-mediated non-radiative recombination and environmental instability. At the perovskite/charge transport layer heterointerface, uncoordinated Pb2+ ions and iodide vacancies act as deep-level traps, reducing open-circuit voltage and fill factor. Conventional passivation strategies often target single defect types, but the synergistic nature of these defects demands multifunctional modifiers. Machine learning offers a data-driven route to screen potential passivation materials, but prediction robustness remains a challenge.
This work introduces a learnable weighted ensemble model (LWEM) that integrates multiple ML algorithms to improve screening accuracy. The model identified 1-benzyl-3-methylimidazolium tetrafluoroborate (BMT) as a promising candidate for planar n-i-p PSCs. Experimental validation confirmed that BMT passivates both Pb2+ and I- defects via an 'ion-coordination dual-lock' mechanism, simultaneously enhancing efficiency and stability. This integrated ML-experimental approach directly addresses the bottleneck of trial-and-error material selection, offering a systematic strategy for developing high-performance PSCs for both outdoor and indoor applications.
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Xinxin Xu, Jiazheng Wang, Qiang Lou, Haibiao Chen, Gehan Amaratunga, Hao Zhang, Jiahao Wu, Maojun Sun, Haocheng Huang, Hang Zhou (2026). A Weighted Ensemble Model for Screening Passivation Materials in High-Efficiency Perovskite Solar Cells. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3968-3
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Frequently Asked Questions
What is the specific role of the 'ion-coordination dual-lock' mechanism in suppressing non-radiative recombination, and how does it quantitatively improve device parameters?
The mechanism involves the imidazolium cation coordinating with iodide vacancies and the tetrafluoroborate anion coordinating with uncoordinated Pb2+ ions, effectively locking both defect sites. This dual passivation reduces trap density, leading to a decrease in non-radiative recombination. Quantitatively, the PCE increased from 22.45% to 24.89% under AM 1.5G, with corresponding improvements in V_OC and FF, indicating reduced voltage and fill factor losses.
How does the LWEM model ensure prediction robustness compared to single ML models, and what is its generalization capability to other material systems?
The LWEM uses a learnable weighting scheme to combine predictions from multiple base models, reducing overfitting and variance. This ensemble approach improves robustness by leveraging the strengths of different algorithms. While the model was trained on a dataset of passivation materials for PSCs, its architecture is generalizable to other material screening tasks, provided sufficient training data is available.
What are the long-term stability metrics of BMT-modified devices under maximum power point tracking and storage conditions?
The abstract states that BMT-modified devices exhibit 'outstanding stability' under long-term storage and maximum power point tracking conditions, but specific numerical data (e.g., percentage of initial PCE retained after X hours) are not provided in the given text. For detailed stability metrics, refer to the full paper.
How does the performance of BMT-modified devices under indoor lighting (41.31% at 1000 lux) compare to existing indoor photovoltaic technologies, and what are the implications for IoT powering?
The 41.31% PCE under 1000 lux LED is among the highest reported for PSCs under indoor illumination, surpassing many organic and dye-sensitized solar cells. This high efficiency at low light intensities makes BMT-modified PSCs highly suitable for powering IoT devices, which require reliable energy harvesting under ambient lighting.
What is the cost and scalability potential of BMT as an interface modifier compared to other passivation materials?
BMT is an imidazolium-based ionic liquid, which are generally low-cost and solution-processable, allowing easy integration into roll-to-roll manufacturing. The ML screening approach reduces the time and cost of experimental trial-and-error, potentially accelerating the development of cost-effective PSC modules. However, a detailed cost analysis is not provided in the abstract.
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