Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning
Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning
复制标题
主动学习引导金属卤化物钙钛矿结晶的尺寸控制
DOI：
10.1021/acs.chemmater.1c03564
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发表时间：
2022
影响因子：
8.6
通讯作者：
Chan, Emory M.
中科院分区：
文献类型：
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作者：
Li, Zhi;Nega, Philip W.;Nellikkal, Mansoor Ani;Dun, Chaochao;Zeller, Matthias;Urban, Jeffrey J.;Saidi, Wissam A.;Schrier, Joshua;Norquist, Alexander J.;Chan, Emory M.
金属卤化物钙钛矿（MHP）衍生物是一类很有前途的光电材料，已经合成了一系列维度，这些维度决定了它们的光电性质并决定了它们的应用。我们展示了一种数据驱动的方法，结合主动学习和高通量实验来发现，控制和理解morpholinium（morph）碘化铅系统中不同维度的相的形成。使用机器人辅助的工作流程，我们合成并表征了两种具有不同光学性质的新型MHP衍生物：一维（1D）morphPbI 3相（[C4 H10 NO][PbI 3]）和二维（2D）（morph）2 PbI 4相（[C4 H10 NO]2[PbI 4]）。为了有效地获取构建1D和2D相形成的反应条件的机器学习（ML）模型所需的数据，数据采集由不同的小批量采样主动学习算法指导，使用预测置信度作为停止标准。查询ML模型发现了对维度控制影响最显著的反应参数。基于这些见解，我们讨论了可能的反应方案，可能会选择性地促进形成不同维度的形态-Pb-I相。这里提出的数据驱动方法，包括使用添加剂来操纵维度，对于控制大反应组成空间内的一系列材料的结晶将是有价值的。
Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-driven approach combining active learning and high-throughput experimentation to discover, control, and understand the formation of phases with different dimensionalities in the morpholinium (morph) lead iodide system. Using a robot-assisted workflow, we synthesized and characterized two novel MHP derivatives that have distinct optical properties: a one-dimensional (1D) morphPbI3phase ([C4H10NO][PbI3]) and a two-dimensional (2D) (morph)2PbI4phase ([C4H10NO]2[PbI4]). To efficiently acquire the data needed to construct a machine learning (ML) model of the reaction conditions where the 1D and 2D phases are formed, data acquisition was guided by a diverse-mini-batch-sampling active learning algorithm, using prediction confidence as a stopping criterion. Querying the ML model uncovered the reaction parameters that have the most significant effects on dimensionality control. Based on these insights, we discuss possible reaction schemes that may selectively promote the formation of morph-Pb-I phases with different dimensionalities. The data-driven approach presented here, including the use of additives to manipulate dimensionality, will be valuable for controlling the crystallization of a range of materials over large reaction-composition spaces.