Quantum Energy Prediction Using Graph Kernel

Vectorial compound representation has played an important role in the recent progress in material property prediction based on machine learning methods. However, the material compounds are originally recorded in the material databases as non-vectorial graph units and space groups. The representation of compounds as handmade vectorial representations is challenging and crucial for the successful application of machine learning. In this study, we attempt to use the random walk graph kernel for material property prediction in which the kernel between graph objects can be automatically constructed from the non-vectorial graph representations in the material informatics databases. By constructing the graph representation efficiently from the raw geometric coordinate data using maximum spanning tree, our method achieves approximately the same prediction power as the conventional vectorial compound representation, and even outperforms it in situations where the number of labeled data is limited. This is well suited for the current material informatics practice where numerous potential material structures have not yet been discovered or annotated. Moreover, we demonstrate that our method maintains a flexible framework that allows the inclusion of domain knowledge, such as electromagnetism, by material scientists.