An Ontology for the Materials Design Domain
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Abstract (arXiv)
In the materials design domain, much of the data from materials calculations are stored in different heterogeneous databases. Materials databases usually have different data models. Therefore, the users have to face the challenges to find the data from adequate sources and integrate data from multiple sources. Ontologies and ontology-based techniques can address such problems as the formal representation of domain knowledge can make data more available and interoperable among different systems. In this paper, we introduce the Materials Design Ontology (MDO), which defines concepts and relations to cover knowledge in the field of materials design. MDO is designed using domain knowledge in materials science (especially in solid-state physics), and is guided by the data from several databases in the materials design field. We show the application of the MDO to materials data retrieved from well-known materials databases.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 4; 2024: 1; 2025: 0; 2026: 2.
8 papers.
- 2023Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Using LLMcited by 5
- 2026The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labscited by 3
- 2024DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materialscited by 2
- 2021CateCom: a practical data-centric approach to categorization of computational modelscited by 1
- 2023HALO: An Ontology for Representing and Categorizing Hallucinations in Large Language Modelscited by 1
- 2023MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterizationcited by 0
- 2023Modeling Dislocation Dynamics Data Using Semantic Web Technologiescited by 0
- 2026Ontology-based knowledge graph infrastructure for interoperable atomistic simulation datacited by 0
- 2026The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labs2 Apr 2026
- 2026Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data31 Mar 2026
- 2024DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materials4 Jan 2024
- 2023HALO: An Ontology for Representing and Categorizing Hallucinations in Large Language Models8 Dec 2023
- 2023Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Using LLM20 Sep 2023
- 2023Modeling Dislocation Dynamics Data Using Semantic Web Technologies13 Sep 2023
- 2023MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterization7 Aug 2023
- 2021CateCom: a practical data-centric approach to categorization of computational models28 Sep 2021