1 code implementation • Technical report, RIMCS LLC 2022 • Takenori Yamamoto
We introduce a large-scale dataset of quantum-mechanically calculated properties of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK).
Ranked #1 on Total Magnetization on OQM9HK
1 code implementation • Technical report, RIMCS LLC 2019 • Takenori Yamamoto
This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.
Ranked #1 on Total Magnetization on OQMD v1.2