Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra
Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra
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DOI：
10.1002/aisy.202200340
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发表时间：
2023-01-20
影响因子：
7.4
通讯作者：
Ahmadi，Mahshid
中科院分区：
文献类型：
--
作者：
Sanchez，Sheryl;Liu，Yongtao;Ahmadi，Mahshid
在过去的几年里，实验室自动化和高通量合成和表征已经成为研究界的前沿。大型数据集需要合适的机器学习技术来有效地分析数据并提取系统的属性。在本文中，金属卤化物钙钛矿（MHP）微晶MAxFA1−xPbI3−xBrx的二元库通过组成和时间相关的光致发光（PL）光谱的低维潜在表示进行了探索。变分自动编码器（VAE）的方法是用来发现系统中的可变性的潜在因素。PL的可变性主要由带隙的组成依赖性控制。同时，次要的变异因素包括与双峰形成相关的相分离。为了克服标准VAE的可解释性限制，引入了基于不变变分（tVAE）和条件自编码器（cVAE）的工作流程。tVAE发现数据内的已知变化因素，例如，由于带隙变化而导致的峰值的（未知）偏移。相反，cVAE施加已知的变化因子，在这种情况下是预期的带隙。tVAE和cVAE共同允许解开数据中存在的潜在机制，这些机制在MHP系统中带来了更深层次的意义和理解。
In the last several years, laboratory automation and high‐throughput synthesis and characterization have come to the forefront of the research community. The large datasets require suitable machine learning techniques to analyze the data effectively and extract the properties of the system. Herein, the binary library of metal halide perovskite (MHP) microcrystals, MAxFA1−xPbI3−xBrx, is explored via low‐dimensional latent representations of composition‐ and time‐dependent photoluminescence (PL) spectra. The variational autoencoder (VAE) approach is used to discover the latent factors of variability in the system. The variability of the PL is predominantly controlled by compositional dependence of the bandgap. At the same time, secondary factor of variability includes the phase separation associated with the formation of the double peaks. To overcome the interpretability limitations of standard VAEs, the workflow based on the translationally invariant variational (tVAEs) and conditional autoencoders (cVAEs) is introduced. tVAE discovers known factors of variation within the data, for example, the (unknown) shift of the peak due to the bandgap variation. Conversely, cVAEs impose known factor of variation, in this case anticipated bandgap. Jointly, the tVAE and cVAE allow to disentangle the underlying mechanisms present within the data that bring a deeper meaning and understanding within MHP systems.