Structure-motif-based material network for functional material discovery.

Data driven approach and machine learning (ML) techniques has shown a significant impact on many disciplines including material science. The success of any ML algorithms relies on the effective representation of the material systems of interest. Structure motifs, essential building components of solid-state materials, have been recognized to be strongly correlated with material properties and are playing a significant role in material design. In this talk, we will discuss the construction of a material network using a structure-motif-based connection measure algorithm, to identify and categorize materials sharing common properties. Structure motif information and the connection types with neighboring motifs are encoded in a feature vector for each motif in a compound. This set of feature vectors are used for similarity measurements between any two material nodes in a general material network. In our initial effort, all the known oxide materials are mapped in a network graph and the connection patterns among these compounds are analyzed. We will discuss the potential use of this motif-based material network for identifying unknown functional materials for technical applications, including transparent conducting oxides, battery materials, and topological materials.