Materials with tailored microstructures are an emerging class of materials with applications to battery electrodes, organic electronics, and biosensors. Tailoring microstructural features that control properties of these materials depends on the ability to first identify the salient features governing properties and next to alter the microstructure accordingly. Choosing robust microstructure representation is pivotal towards completing both steps. In this paper, we focus on the first step and present the methodology for extracting and quantifying a local set of microstructural features covering topology, shape and size aspects of the microstructure. We use the myriad of tools to facilitate the process of clustering the common features in a given morphology and back tracking them in the input morphology opening avenues for the morphology optimization.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by King Abdullah University of Science and Technology (KAUST) and National Science Foundation (1906344). OW acknowledges the support provided by the Center for Computational Research at the University at Buffalo.