Atomic descriptors generated from coordination polyhedra in crystal structures
Yuki Inada, Yukari Katsura, Masaya Kumagai, Kaoru Kimura
Abstract
We developed atomic descriptors from local crystal structures, which will facilitate researchers’ use of machine learning to predict the properties of inorganic materials via materials informatics. We applied singular value decomposition to the occurrence matrix of local coordination polyhedra in crystal structures. We generated two atomic descriptors, each based on the coordination atoms and topology of the coordination polyhedra. As a result of atomic clustering using these descriptors, the composition descriptor proposed in previous research depends on the similarity between same-group atoms in the periodic table. In contrast, when using our original descriptors based on the coordination atoms and topology of the coordination polyhedra, the similarity between adjacent atoms in the periodic table as well as the similarity between same-group atoms was pertinent. When we used machine learning to predict the formation energy and band gap using these descriptors as inputs, the prediction accuracy and generalization ability increased compared with using a physical property descriptor.