Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 10 Jul 2022.
Builds on
4 of 23 references.
References · 4
- 2020Knowledge Graph Embedding for Link Prediction: A Comparative Analysiscited by 85
- 2022An Open Challenge for Inductive Link Prediction on Knowledge Graphscited by 26
- 2021MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworkscited by 11
- 2017Automatically Extracting Action Graphs from Materials Science Synthesis Procedurescited by 6
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Citing papers per year: 2023: 1.
1 paper.