Introducing self-organized maps (SOM) as a visualization tool for materials research and education

Abstract

Materials informatics is an emerging discipline that opens a new paradigm of science: data-driven materials discovery. The visualization of high dimensional material properties data is challenging due to a lack of appropriate tools. This article describes a workflow for using Self-Organizing Maps (SOM) as a time- and cost-efficient method of visualizing and validating property relationships between materials. SOM is a machine learning technique that uses dimensionality reduction based on similarities between properties to visualize the relationships between materials in a high-dimensional dataset. While the conventionally used visualization method, Ashby plot, displays a pair of material’s properties, SOM is a technique that can display multiple properties that are effective for high-dimensional materials correlation studies. In this paper, SOM was used to validate property correlations among 21 material properties in a dataset of over 398 commercial materials. We employed U-matrix map, clustering map and heatmaps to visualize the SOM we trained. The clustering and heat maps produced from SOM were used to identify unique materials and infer correlations between material properties and fundamental material structure. We have also shown that SOM method can provide multiple layers of interpretation through visualizing not only numerical properties but also categorical information of materials. Lastly, we have used SOM to provide new insight into the quantity of Grüneisen parameter across metals and ceramics. Through these examples, it is demonstrated that SOM can be used for different types of materials analyses for various applications. This paper advocates SOM as an integrated approach to study material property relationships that can be used for both materials education and discovery.