发表机构
Forschungszentrum Jülich; RWTH Aachen（于利希研究中心; 亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于人工神经网络的参数估计工作流程，用于快速分析卤化物钙钛矿薄膜的瞬态光致发光衰减，实现多维参数空间快速扫描并降低参数不确定性。
AI 中文摘要
从实验数据中提取材料参数通常具有挑战性，尤其是在无法使用可逆解析方程将数据与目标物理量联系起来的情况下。如果实验与材料参数之间的联系由一组非线性微分方程在数学上给出，那么在传统的拟合过程中必须反复求解这些方程，导致优化时间长且对参数不确定性的洞察有限。在此，我们提出了一种专门针对铅卤化物钙钛矿薄膜上进行的瞬态光致发光测量的参数估计工作流程。该工作流程利用人工神经网络加速实验与模拟之间的快速比较。该方法的一个优势是能够快速扫描多维材料参数空间，并识别参数之间的相关性，从而为卤化物钙钛矿中非辐射复合的物理机制提供见解。最后，我们比较了稳态和瞬态光致发光，并展示了如何通过将稳态数据纳入参数估计工作流程来减少诸如缺陷密度等参数的不确定性。
英文摘要
Extracting material parameters from experimental data is often challenging if no invertible analytical equation can be used to link the data with the quantities of interest. If the link between experiment and material parameters is given mathematically by a set of non-linear differential equations, these must be solved repeatedly during the traditional fitting procedure, resulting in long optimization times and limited insight into parameter uncertainty. Here, we present a parameter estimation workflow specifically aimed at transient photoluminescence measurements performed on lead-halide perovskite films. This workflow is accelerated using artificial neural networks for rapid comparison between experiment and simulation. An advantage of the method is the ability to rapidly scan multidimensional material parameter spaces and identify correlations between parameters that provide insights into the physics of non-radiative recombination in halide perovskites. Finally, we compare steady-state and transient photoluminescence and show how uncertainty in parameters such as the defect density can be reduced by including steady-state data in the parameter estimation workflow.
CommentsMain: 49 pages, 10 figures SI: 20 pages, 16 figures
