Perovskites have seen significant research interest in the last decade. As ternary and quaternary compounds, their chemical space is exceptionally large, yet perovskite development has been limited to a restricted set of chemical constituents often discovered through trial and error. Here, we report a high-throughput experimental framework for the discovery of new perovskite single crystals. We use machine learning (ML) to guide the sequence of ever-improved robotic synthetic trials. We perform high-throughput syntheses of perovskite single crystals with a protein crystallization robot and characterize the outcomes with the aid of convolutional neural network-based image recognition. We then use an ML model to predict the optimal conditions for the synthesis of a new perovskite single crystal, enabling us to report the first synthesis of (3-PLA)₂PbCl₄.This material exhibits strong blue emission, illustrating the applicability of the method in identifying new optoelectronic materials.

在过去的十年中，钙钛矿受到了广泛的研究兴趣。作为三元和四元化合物，它们的化学空间异常大，但钙钛矿的开发仅限于通过反复试验发现的一组有限的化学成分。在这里，我们报告了一种高通量实验框架，用于发现新的钙钛矿单晶。我们使用机器学习（ML）来指导经过不断改进的机器人综合试验的顺序。我们使用蛋白质结晶机器人执行钙钛矿单晶的高通量合成，并借助基于卷积神经网络的图像识别来表征结果。然后，我们使用ML模型来预测合成新钙钛矿单晶的最佳条件，从而使我们能够报告（3-PLA）的首次合成₂ PbCl _4。这种材料表现出强烈的蓝色发射，说明该方法在识别新的光电材料中的适用性。