# Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

arXiv: https://arxiv.org/abs/2404.03080 · HTML: https://mlsanity.com/paper/2404.03080/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [DARWIN Series: Domain Specific Large Language Models for Natural Science](https://mlsanity.com/paper/2308.13565/) (2023)
- [Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Using LLM](https://mlsanity.com/paper/2309.11361/) (2023)
- [Benchmark datasets for biomedical knowledge graphs with negative statements](https://mlsanity.com/paper/2307.11719/) (2023)

## Built on by (most cited first, 15 of 15)

- [MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph](https://mlsanity.com/paper/2508.12393/) (2025)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [TritonDFT: Automating DFT with a Multi-Agent Framework](https://mlsanity.com/paper/2603.03372/) (2026)
- [DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion](https://mlsanity.com/paper/2507.11229/) (2025)
- [Constructing material network representations for intelligent amorphous alloys design](https://mlsanity.com/paper/2507.16336/) (2025)
- [CITE: A Comprehensive Benchmark for Heterogeneous Text-Attributed Graphs on Catalytic Materials](https://mlsanity.com/paper/2508.15392/) (2025)
- [ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature](https://mlsanity.com/paper/2510.20362/) (2025)
- [OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion](https://mlsanity.com/paper/2603.11820/) (2026)
- [Towards Multimodal Graph Large Language Model](https://mlsanity.com/paper/2506.09738/) (2025)
- [Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining](https://mlsanity.com/paper/2506.12516/) (2025)
- [Linking heterogeneous microstructure informatics with expert characterization knowledge through customized and hybrid vision-language representations for industrial qualification](https://mlsanity.com/paper/2508.20243/) (2025)
- [Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model](https://mlsanity.com/paper/2509.06093/) (2025)
- [Element2Vec: Build Chemical Element Representation from Text for Property Prediction](https://mlsanity.com/paper/2510.13916/) (2025)
- [From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle](https://mlsanity.com/paper/2510.27369/) (2025)
- [Automatically Building and Updating a Knowledge Graph of MLIP Models](https://mlsanity.com/paper/2610.09644/) (2026)

## Abstract (arXiv)

Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges to the efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovation. Addressing these challenges, the integration of artificial intelligence with materials science has opened avenues for accelerating the discovery process, though it also demands precise annotation, data extraction, and traceability of information. To tackle these issues, this article introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural language processing techniques integrated with large language models to extract and systematically organize a decade's worth of high-quality research into structured triples, contains 162,605 nodes and 731,772 edges. MKG categorizes information into comprehensive labels such as Name, Formula, and Application, structured around a meticulously designed ontology, thus enhancing data usability and integration. By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods. This structured approach not only streamlines materials research but also lays the groundwork for more sophisticated science knowledge graphs.