Structure motif-centric learning framework for inorganic crystalline systems

Physical principles can be incorporated in a machine learning architecture as a fundamental setup to develop artificial intelligence for inorganic materials. In a new report now on Science Advances, Huta R. Banjade, and a research team in physics, computer and information science and nanoscience in the U.S. and Belgium proposed structure motifs in inorganic crystals to serve as a central input to a machine learning framework. The team demonstrated how the presence of structure motifs and their connections in a large set of crystalline compounds could be converted into unique vector representations via an unsupervised learning algorithm. They accomplished this by creating a motif-centric leaning framework by combining motif information with atom-based graph neural networks to form an atom-motif dual graph network (AMDNet). The setup accurately predicted the electronic structure of metal oxides such as bandgaps. The work illustrates a method to design graph neural network learning architectures to investigate complex materials beyond atom physical properties.