Microstructure taxonomy based on spatial correlations: Application to microstructure coarsening

To build materials knowledge, rigorous description of the material structure and associated tools to explore and exploit information encoded in the structure are needed. These enable recognition, categorization and identification of different classes of microstructure and ultimately enable to link structure with properties of materials. Particular interest lies in the protocols capable of mining the essential information in large microstructure datasets and building robust knowledge systems that can be easily accessed, searched, and shared by the broader materials community. In this paper, we develop a protocol based on automated tools to classify microstructure taxonomies in the context of coarsening behavior which is important for long term stability of materials. Our new concepts for enhanced description of the local microstructure state provide flexibility of description. The mathematical description of microstructure that capture crucial attributes of the material, although central to building materials knowledge, is still elusive. The new description captures important higher order spatial information, but at the same time, allows down sampling if less information is needed. We showcase the classification protocol by studying coarsening of binary polymer blends and classifying steady state structures. We study several microstructure descriptions by changing the microstructure local state order and discretization and critically evaluate their efficacy. Our analysis revealed the superior properties of microstructure representation is based on the first order-gradient of the atomic fraction.