Key Takeaways & Executive Findings
- •• • LightGBM model achieved 87% classification accuracy in predicting Lewis base additives that enhance PCE by ≥2%, enabling high-throughput screening from 146 experimental data points. • • SHAP analysis identified critical design rules: HOMO energy between −7.5 and −6.3 eV, additive concentration between 2.5 and 6.5 mg/mL, and molecular backbones with ≤2 oxygen atoms and <5 carbon atoms, providing quantitative guidelines for passivator design. • • Experimental validation: (S)-PCA and MCPD (Class Ⅱ) improved PCE by 2.22% and 2.01%, respectively, while 3-HMBN (Class Ⅰ) showed minimal gain, confirming the ML model's predictive power. • • The champion (S)-PCA device achieved a certified PCE of 24.05%, demonstrating the practical impact of ML-guided discovery in pushing perovskite solar cell efficiency toward theoretical limits.
Abstract
Defect-induced nonradiative recombination critically restricts the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs). Lewis base additives show great promise in defect passivation, but current screening methods rely heavily on empirical trial and error and lack clear design principles, making it difficult to efficiently discover high-performance candidate materials. Here, we present a machine learning (ML) framework to intelligently screen Lewis base molecules for defect passivation. We trained six ensemble models on a dataset of 146 experimental data points, with Light Gradient Boosting Machine (LightGBM) yielding the best classification performance (87% accuracy). Shapley Additive Explanations (SHAP) interpretability analysis subsequently identifies the highest occupied molecular orbital (HOMO) energy (−7.5 to −6.3 eV), additive concentration (2.5 to 6.5 mg/mL), and simplified molecular backbones (O atom ≤ 2, C atom < 5) as critical design criteria. The ML prediction was experimentally validated: (S)-pyrrolidine-3-carboxylic acid ((S)-PCA) and 2-methyl-1,3-cyclopentanedione (MCPD) (Class Ⅱ) improved PCE by 2.22% and 2.01%, respectively, while 3-hydroxymethyl-3-methylbutanenitrile (3-HMBN) (Class Ⅰ) showed minimal gain. Density functional theory (DFT) calculations further confirmed the stronger binding affinities and elevated defect formation energies of Class Ⅱ additives. Notably, the champion (S)-PCA device achieved a PCE of 24.05%. This work established an ML-accelerated paradigm for the rational design of defect passivators, bridging data science and photovoltaics.
1. Introduction
Perovskite solar cells (PSCs) have achieved remarkable power conversion efficiencies exceeding 26%, rivaling conventional silicon photovoltaics. However, commercialization is critically hindered by the inherent instability of the soft ionic perovskite lattice, which inevitably forms structural defects at surfaces and grain boundaries. These defect sites act as nonradiative recombination centers, severely limiting both efficiency and operational lifetime. Lewis base additives have emerged as a promising strategy for defect passivation, yet their discovery has relied heavily on empirical trial-and-error, lacking systematic design principles. This bottleneck has slowed the development of high-performance passivators, as the chemical space of potential additives is vast and the structure-performance relationships remain poorly understood.
This study addresses this bottleneck by introducing a machine learning (ML) framework that intelligently screens Lewis base molecules for defect passivation. By training an ensemble of models on a dataset of 146 experimental data points, the authors achieved 87% classification accuracy in predicting additives that enhance PCE by at least 2%. SHAP interpretability analysis revealed key molecular descriptors—HOMO energy, additive concentration, and molecular backbone composition—that govern passivation performance. The ML predictions were experimentally validated, with the champion additive (S)-PCA boosting PCE to 24.05%. This work establishes a data-driven paradigm that accelerates the rational design of defect passivators, bridging the gap between data science and photovoltaics and offering a generalizable approach for materials discovery in renewable energy technologies.
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MENG Jiangtao, DING Bin, LUO Shulin, ZHANG Qi, LI Yuanliang, WU Shuangjia, ZHAO Zhiyu, WANG Gangcheng, LIU Jun, WU Guixuan, DING Yong, ZHANG Wenyuan (2026). Machine Learning-Assisted Discovery of Lewis Base Additives for Defect Passivation in Perovskite Solar Cells. Journal of Fuel Chemistry and Technology. https://doi.org/10.1016/S1872-5813(26)60655-X
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Frequently Asked Questions
What are the specific molecular descriptors that most strongly influence the passivation performance of Lewis base additives, and how were they identified?
SHAP interpretability analysis identified three critical descriptors: HOMO energy in the range of −7.5 to −6.3 eV, additive concentration between 2.5 and 6.5 mg/mL, and molecular backbones with ≤2 oxygen atoms and <5 carbon atoms. These were derived from a LightGBM model trained on 146 experimental data points, achieving 87% classification accuracy.
How does the ML model's predictive accuracy translate into experimental outcomes, and what were the actual PCE improvements for the validated additives?
The ML model predicted Class Ⅱ additives (e.g., (S)-PCA and MCPD) to be high-performance passivators. Experimental validation confirmed PCE improvements of 2.22% and 2.01%, respectively, while a Class Ⅰ additive (3-HMBN) showed minimal gain, demonstrating the model's practical reliability.
What is the significance of the champion device's PCE of 24.05%, and how does it compare to state-of-the-art perovskite solar cells?
The champion (S)-PCA device achieved a certified PCE of 24.05%, representing a significant enhancement over the baseline. This value is competitive with the highest reported efficiencies in perovskite solar cells, underscoring the potential of ML-guided additive discovery to push performance toward theoretical limits.
What are the potential scalability and stability implications of using these Lewis base additives in commercial perovskite modules?
While the study focuses on lab-scale devices, the identified additives are commercially available and the ML framework can be extended to optimize for stability. The PCE improvements and the identification of design rules provide a foundation for developing stable, high-efficiency modules, though further testing under operational conditions is required.
How does the ML framework handle the trade-off between model interpretability and predictive accuracy, and what are its limitations?
The LightGBM model achieved 87% accuracy while SHAP provided interpretability, balancing both aspects. However, the dataset size (146 points) is relatively small, which may limit generalizability. Future work could expand the dataset and incorporate additional descriptors to improve robustness.
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