Skeletal-based microstructure representation and featurization through descriptors
Skeletal-based microstructure representation and featurization through descriptors
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DOI：
10.1016/j.commatsci.2022.111668
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发表时间：
2022-08-11
影响因子：
3.3
通讯作者：
Wodo, Olga
中科院分区：
文献类型：
--
作者：
Jivani, Devyani;Wodo, Olga
使用机器学习方法建模过程-结构-性质关系已成为材料设计和发现的有价值的推动因素。然而，机器学习模型严重依赖于材料结构的特征化。本文介绍了一种计算通用拓扑和形态描述符的微结构特征化框架。该框架依赖于我们的骨骼微观结构表示。该表示允许无缝计算的拓扑描述符，这是本文的主要focus.To证明我们的特征化框架的有效性，我们将其与特征选择方法相结合，以建立有机光致发光（OPV）的结构-性质模型。为了这个目标，我们确定了一组突出的描述符，并构建了高精度的结构-性质图。
Modeling process-structure-property relationships using machine learning methods has become a valuable enabler for materials design and discovery. However, the machine learning models rely heavily on the featurization of the materials' structure. This paper introduces a microstructure featurization framework to compute generic topological and morphological descriptors. The framework relies on our skeletal microstructure representation. The representation allows for the seamless calculation of topological descriptors, which is the main focus of this paper.To demonstrate the efficacy of our featurization framework, we couple it with a feature selection method to establish the structure-property model for organic photovoltaics (OPV). For this goal, we identify a salient set of descriptors and construct the structure-property map with high accuracy.