Achieving high-performance inverted perovskite solar cells (PSCs) still remains a significant challenge, necessitating innovative approaches in materials selection and manufacturing technique optimization of perovskites. In this work, we unveil a paradigm shift in PSCs optimization. Through a judicious selection from a repertoire of 60 perovskite variants, we identified a composition with exemplary optical, thermal and electrical stability. Employing Bayesian machine learning, we navigated a labyrinth of over 1 billion process conditions, culminating in a record-breaking efficiency within a mere 80 iterations. Finally, the integration of bespoke in situ polymerized ionic molecules allowed us to further augment performance of inverted PSCs, reaching an unparalleled power conversion efficiency of 25.76% (certified at 25.21%). The PSCs retained 94% of the initial efficiency after continuous operation in a nitrogen atmosphere at 65 °C for 1920 hours. This work not only redefines the benchmarks for PSCs but also illuminates the path forward for photovoltaic innovations.

中文翻译：

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结合组件筛选、机器学习和分子工程设计高性能倒置钙钛矿太阳能电池


实现高性能倒置钙钛矿太阳能电池（PSC）仍然是一个重大挑战，需要在钙钛矿的材料选择和制造技术优化方面采取创新方法。在这项工作中，我们揭示了 PSC 优化的范式转变。通过从 60 种钙钛矿变体中进行明智的选择，我们确定了一种具有出色的光学、热和电稳定性的组合物。利用贝叶斯机器学习，我们在超过 10 亿个过程条件的迷宫中导航，最终在短短 80 次迭代内实现了破纪录的效率。最后，定制的原位聚合离子分子的集成使我们能够进一步增强倒置 PSC 的性能，达到无与伦比的功率转换效率 25.76%（认证为 25.21%）。在 65 °C 的氮气气氛中连续运行 1920 小时后，PSC 保持了 94% 的初始效率。这项工作不仅重新定义了太阳能电池的基准，而且还阐明了光伏创新的前进道路。