← 研踪 PaperTrailarXiv:2611.16750 · v1
Interpretable machine learning diagnosis of degradation mechanisms in lead-free $RbSn_{0.5}Ge_{0.5}I_{3}$ solar cells from temperature- and illumination-dependent electrical measurements
首次提交 2026-11-12 21:03
版本更新 2026-11-12 21:03
雷达观测 2026-11-13 14:15
摘要
Machine learning coupled with device simulation is widely used to optimize thin-film solar cells, but almost exclusively in the forward direction, from device parameters to predicted performance. The inverse problem encountered after fabrication, namely identifying the mechanism that limits a given device, is still addressed by manual inspection of characterization data. In this work, both directions are examined for lead-free $RbSn_{0.5}Ge_{0.5}I_{3}$ solar cells using approximately 42,000 SCAPS-1D simulations. Replacing the Spiro-OMeTAD hole transport layer with CuI increases the simulated power conversion efficiency from 19.6\% to 21.6\%, almost entirely through the fill factor. Surrogate models trained and tested on a factorial parameter grid substantially overstate their predictive accuracy. An XGBoost regressor attains $R^2 = 0.9997$ on held-out grid points but only 0.58 on independently sampled devices, whereas training on a Latin hypercube design of comparable size restores $R^2 = 0.995$. For the inverse problem, a multi-label classifier identifies which of six degradation mechanisms are present from simulated temperature- and illumination-dependent open-circuit-voltage measurements. It achieves a macro $F_1$ score of 0.938, compared to 0.687 for a calibrated physics-rule baseline. The classifier does not, however, generalize to combinations of mechanisms absent from the training set.
作者备注：Under review in RSC Advances
观测到的版本
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v1 · 2026-11-12 21:03 · 最新观测版本
Interpretable machine learning diagnosis of degradation mechanisms in lead-free $RbSn_{0.5}Ge_{0.5}I_{3}$ solar cells from temperature- and illumination-dependent electrical measurements
Machine learning coupled with device simulation is widely used to optimize thin-film solar cells, but almost exclusively in the forward direction, from device parameters to predicted performance. The inverse problem encountered after fabrication, namely identifying the mechanism that limits a given device, is still addressed by manual inspection of characterization data. In this work, both directions are examined for lead-free $RbSn_{0.5}Ge_{0.5}I_{3}$ solar cells using approximately 42,000 SCAPS-1D simulations. Replacing the Spiro-OMeTAD hole transport layer with CuI increases the simulated power conversion efficiency from 19.6\% to 21.6\%, almost entirely through the fill factor. Surrogate models trained and tested on a factorial parameter grid substantially overstate their predictive accuracy. An XGBoost regressor attains $R^2 = 0.9997$ on held-out grid points but only 0.58 on independently sampled devices, whereas training on a Latin hypercube design of comparable size restores $R^2 = 0.995$. For the inverse problem, a multi-label classifier identifies which of six degradation mechanisms are present from simulated temperature- and illumination-dependent open-circuit-voltage measurements. It achieves a macro $F_1$ score of 0.938, compared to 0.687 for a calibrated physics-rule baseline. The classifier does not, however, generalize to combinations of mechanisms absent from the training set.
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