Data-driven approaches for materials design and selection have accelerated materials discovery along with the upsurge of machine learning applications. We report here a prediction-to-lab-scale synthesis of cubic phase triple-cation lead halide perovskites guided by a machine learning perovskite stability predictor. The starting double-cation perovskite resulting from the incorporation of 15% dimethylammonium (DMA) in methylammonium lead triiodide suffers from significant deviation from the perovskite structure. By analyzing the X-ray diffraction and scanning electron microscopy, we confirmed that it is possible to recover the perovskite structure with the cubic phase at room temperature (RT) while minimizing the iterations of trial-and-error by adding <10 mol % of cesium cation additives, as guided by the machine learning predictor. Our conclusions highly support the cubic-phase stabilization at RT by controlling the stoichiometric ratio of various sized cations. This prediction-to-lab-scale synthesis approach also enables us to identify room for improvements of the current machine learning predictor to take into consideration the cubic phase stability as well as phase segregation.

中文翻译：

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机器学习加速混合阳离子钙钛矿薄膜立方结构的恢复

数据驱动的材料设计和选择方法，随着机器学习应用的兴起，加速了材料的发现。我们在这里报告由机器学习钙钛矿稳定性预测器指导的立方相三阳离子卤化铅钙钛矿的实验室规模预测合成。由在甲基碘化三碘化铅中掺入15％二甲基铵（DMA）产生的起始双阳离子钙钛矿遭受钙钛矿结构的明显偏离。通过分析X射线衍射和扫描电子显微镜，我们确认可以在室温（RT）下恢复立方相的钙钛矿结构，同时通过添加<10 mol％的铯阳离子添加剂，由机器学习预测器指导。通过控制各种尺寸阳离子的化学计量比，我们的结论高度支持室温下的立方相稳定化。这种预测到实验室规模的合成方法还使我们能够确定改进当前机器学习预测器的空间，同时考虑到立方相的稳定性以及相分离。