Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies
This work addresses the need for computational tools to screen MOF candidates for applications like gas storage and catalysis, though it appears incremental as it applies existing knowledge graph methods to a specific domain.
The paper tackles the challenge of managing the vast number of synthesized and potential Metal-Organic Framework (MOF) structures by building an open knowledge graph (MOF-KG) to facilitate prediction, discovery, and synthesis, presenting case studies on its construction and use for uncovering new knowledge.
Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, catalysis, and drug delivery. The Cambridge Structural Database (CSD) reports 10,636 synthesized MOF crystals which in addition contains ca. 114,373 MOF-like structures. The sheer number of synthesized (plus potentially synthesizable) MOF structures requires researchers pursue computational techniques to screen and isolate MOF candidates. In this demo paper, we describe our effort on leveraging knowledge graph methods to facilitate MOF prediction, discovery, and synthesis. We present challenges and case studies about (1) construction of a MOF knowledge graph (MOF-KG) from structured and unstructured sources and (2) leveraging the MOF-KG for discovery of new or missing knowledge.