# Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing

arXiv: https://arxiv.org/abs/2101.01508 · HTML: https://mlsanity.com/paper/2101.01508/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2021.

## Built on by (most cited first, 16 of 16)

- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [Are LLMs Ready for Real-World Materials Discovery?](https://mlsanity.com/paper/2402.05200/) (2024)
- [MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling](https://mlsanity.com/paper/2305.08264/) (2023)
- [Foundational Large Language Models for Materials Research](https://mlsanity.com/paper/2412.09560/) (2024)
- [MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models](https://mlsanity.com/paper/2308.09115/) (2023)
- [Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction](https://mlsanity.com/paper/2310.08383/) (2023)
- [MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science](https://mlsanity.com/paper/2410.15126/) (2024)
- [MatGD: Materials Graph Digitizer](https://mlsanity.com/paper/2311.12806/) (2023)
- [Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain](https://mlsanity.com/paper/2304.14745/) (2023)
- [Incorporating Domain Knowledge into Materials Tokenization](https://mlsanity.com/paper/2506.11115/) (2025)
- [Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?](https://mlsanity.com/paper/2407.08922/) (2024)
- [Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge](https://mlsanity.com/paper/2502.16451/) (2025)
- [Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms](https://mlsanity.com/paper/2204.12309/) (2022)
- [NLP for Knowledge Discovery and Information Extraction from Energetics Corpora](https://mlsanity.com/paper/2402.06964/) (2024)
- [Sustainable Materials Discovery in the Era of Artificial Intelligence](https://mlsanity.com/paper/2601.21527/) (2026)
- [Towards Agentic Defect Reasoning: A Graph-Assisted Retrieval Framework for Laser Powder Bed Fusion](https://mlsanity.com/paper/2604.04208/) (2026)

## Abstract (arXiv)

Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA), providing a way to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots are presented using the 'Caption Cluster Plot' (CCP), which provides direct access to the images buried in the papers. Finally, we combine the LDA and CCP with the chemical elements occurring in the manuscript to present an 'Elemental map', a topical and image-wise distribution of chemical elements in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition-structure-processing-property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery.