Abstract Scope |
The holy grail of materials science is to discover the mechanism governing the material properties and describe them in terms of a small set of physically meaningful descriptors. The discovery and exploration of materials and their properties critically depend on the availability of easily computable descriptors. In this talk, we present our framework to compute a library of generic descriptors for micrographs. We describe our microstructure representation that is based on the graph and skeleton and enables microstructure characterization in terms of shape (i.e., morphology), geometry, and connectedness (i.e., topology). We explain how this work lays the foundation for machine learning of microstructure-property relationships and enables information fusion between multiple scales. We showcase our framework using examples from organic electronics. |