Feature Engineering for Microstructure–Property Mapping in Organic Photovoltaics
Feature Engineering for Microstructure–Property Mapping in Organic Photovoltaics
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DOI：
10.1007/s40192-022-00267-2
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发表时间：
2021-11
影响因子：
3.3
通讯作者：
S. Hashemi;B. Ganapathysubramanian;S. Casey;Jie Su;S. Kalidindi
中科院分区：
文献类型：
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作者：
S. Hashemi;B. Ganapathysubramanian;S. Casey;Jie Su;S. Kalidindi
将有机光伏（OPV）薄膜的高度复杂的形态与其电荷传输特性联系起来对于实现促进具有成本效益的能量收集的高性能材料系统至关重要。在本文中，目前的材料知识系统（MKS）的框架进行了扩展，使其能够建立降阶高保真的结构-性能的联系OPV薄膜。具体而言，需要进行以下扩展：（i）数字图像处理算法的适当应用，以识别控制电荷传输现象的OPV微结构中的显著局部材料状态，（ii）计算高效的特征工程，其不仅利用2点空间相关性和主成分分析，而且还利用两个新的基于距离的度量，以及（iii）将高斯过程（laGP）的局部化版本与主动学习Cohn（ALC）一起成功应用于构建将OPV微结构与其短路电流联系起来的期望的替代模型。这表明，扩展的MKS框架可以产生高保真的OPV膜的结构-性质的联系。
Linking the highly complex morphology of organic photovoltaic (OPV) thin films to their charge transport properties is critical for achieving high performance material systems that facilitate cost-efficient energy harvesting. In this paper, the current Materials Knowledge Systems (MKS) framework was extended so that it was able to establish reduced-order high-fidelity structure–property linkages for OPV films. Specifically, the following extensions were needed: (i) the proper application of digital image processing algorithms to identify the salient local material states in OPV microstructures controlling the charge transport phenomenon, (ii) computationally efficient feature engineering that not only utilized 2-point spatial correlations and principal component analysis, but also two new distance-based metrics, and (iii) the successful application of a localized version of the Gaussian process (laGP) together with an active learning Cohn (ALC) for building the desired surrogate models linking the OPV microstructures to their short-circuit currents. It is demonstrated that the extended MKS framework can produce high-fidelity structure–property linkages for OPV films.