Materials Map Integrating Experimental and Computational Data through Graph-Based Machine Learning for Enhanced Materials Discovery

Materials informatics (MI), which emerges from the integration of materials science and data science, is expected to greatly streamline material discovery and development. The data used for MI are obtained from both computational and experimental studies, while their integration remains challenging. In our previous study, we reported the integration of these datasets by applying a machine learning model that captures trends hidden in the experimental datasets to compositional data stored in the computational database. In this study, we use the obtained data to construct materials maps, which visualize the relation in the structural features of materials, aiming to support study by the experimental researchers. The map is constructed using a MatDeepLearn (MDL) framework, which implements the graph-based representation of material structures, deep learning, and dimensional reduction for map construction. We evaluate the obtained materials maps through statistical analysis and found that MDL using message passing neural network (MPNN) architecture enables efficient extraction of features that reflect the structural complexity of materials. Moreover, we found that this advantage does not necessarily translate into improved accuracy in the prediction of material properties. We assume this unexpected outcome to the high learning performance inherent in MPNN, which can contribute to the structuring of data points within the materials map.