Visualising multi-dimensional structure/property relationships with machine learning

Abstract

Data visualisation is an important part of understanding the distributions, trends, correlations and relationships in materials data sets, as well as communicating results to others. Traditionally visualisation has been straightforward, particularly when studying single-structure/single-property relationships. It is not so straightforward when confronted with a materials data set represented by a large number of features, and containing multi-structure/multi-property relationships. Here we use Kohonen networks, or self-organising maps, to aid in the visualise sets of silver and platinum nanoparticles based on structural similarity and overlay functional properties to reveal hidden patterns and structure/property relationships. We compare these maps to a popular alternative dimension reduction method and find them superior for our cases where the structure/property relationships are highly nonlinear, and the data set is imbalanced, as they often are in materials science.