Universal graph-based identifiers of chemical structures for linking large material databases

The rapid expansion of computational databases of materials, driven by advancements in computational speeds, storage, and algorithm development, has enabled the unified treatment of materials with various compositions and structures. However, the lack of universal identifiers for chemical structures impedes the efficient utilization of databases. To address this issue, we propose an identifier named Graph ID, which is based on chemically intuitive atomic distances and a novel node labeling scheme. Graph ID exhibits scalability and can accurately differentiate between structurally distinct materials. It can be applied to various chemical structures, including bulk crystals, surface structures, and molecules. The code to generate Graph ID is available as open-source software, which will contribute to accelerating materials simulations and data analysis. The lack of universal identifiers for chemical structures impedes efficient utilization of large databases. Here, the authors introduce Graph ID based on atomistic distances in the structure allowing efficient distinguishing between diverse materials.