Machine learning (ML) is gaining more attention in photovoltaic research and will be a vital tool in reaching record-high power conversion efficiencies (PCE) in the near future. One area, where ML is significantly beneficial is reducing the number of experiments needed to find the optimum combination of parameters in solar cell fabrication. Bayesian optimization (BO) provides routes for quickly identifying optimum parameters in problems with large parameter space. In this work, BO algorithms utilizing previous knowledge are demonstrated to result in faster optimization of tandem solar cells, a technology rapidly gaining more interest due to its potential to deliver record-high performance. Namely, it is shown that in the space of all possible parameter combinations that take ≈88 years to evaluate, optimum PCE can be obtained in 20 min. Moreover, it is demonstrated that methods utilizing previous knowledge outperform those that do not by yielding an increase of ≈8.9%_abs. in the 1st iteration and requiring 5× less time to reach a target PCE of 38.5% across 20 different trials. Results from this work help accelerate the development of tandem solar cells by removing the need for large numbers of experiments in identifying optimum parameters.

中文翻译：

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贝叶斯优化与快速开发单片串联太阳能电池的经验：模拟案例研究

机器学习 (ML) 在光伏研究中越来越受到关注，并将成为在不久的将来达到创纪录的高功率转换效率 (PCE) 的重要工具。机器学习显着受益的一个领域是减少在太阳能电池制造中找到最佳参数组合所需的实验数量。贝叶斯优化（BO）提供了在大参数空间问题中快速识别最佳参数的途径。在这项工作中，利用先前知识的 BO 算法被证明可以更快地优化串联太阳能电池，这项技术因其提供创纪录的高性能的潜力而迅速获得更多关注。也就是说，结果表明，在需要约 88 年评估的所有可能参数组合的空间中，可以在 20 分钟内获得最佳 PCE。此外，事实证明，利用先前知识的方法优于那些不利用先前知识的方法，增加了约 8.9% 的_绝对值。在第一次迭代中，在 20 次不同的试验中，需要 5 倍的时间才能达到 38.5% 的目标 PCE。这项工作的结果无需进行大量实验来确定最佳参数，从而有助于加速串联太阳能电池的开发。