Motif-based machine learning for crystalline materials

With the development of advanced algorithms and improvements in computational power, machine learning (ML) has been widely successful in predicting various physical and chemical properties of materials. The success of any ML model mainly depends on the good representation of the input data, and there have been surging interests in identifying effective representations for crystalline materials. In this talk, we propose a novel representation of crystalline solid-state materials (such as complex metal oxides) as graphs composed of structure motifs. This motif-based representation serves as input to a graph convolutional network for the learning and prediction of material properties, such as bandgaps and formation energies. Our test results indicate that, when combined with atomic information and related networks, the inclusion of motif information in the network architecture improves the prediction performance, especially for complex oxide materials.