# From Text to Insight: Large Language Models for Materials Science Data Extraction

arXiv: https://arxiv.org/abs/2407.16867 · HTML: https://mlsanity.com/paper/2407.16867/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2024.

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

- [SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning](https://mlsanity.com/paper/2409.05556/) (2024)
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- [MatExpert: Decomposing Materials Discovery by Mimicking Human Experts](https://mlsanity.com/paper/2410.21317/) (2024)
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- [GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design](https://mlsanity.com/paper/2602.07491/) (2026)
- [Leveraging Vision Capabilities of Multimodal LLMs for Automated Data Extraction from Plots](https://mlsanity.com/paper/2503.12326/) (2025)
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- [Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation](https://mlsanity.com/paper/2504.00187/) (2025)
- [A System for Name and Address Parsing with Large Language Models](https://mlsanity.com/paper/2601.18014/) (2026)
- [The Impact of Modern AI in Metadata Management](https://mlsanity.com/paper/2501.16605/) (2025)
- [Evaluating Structured Output Robustness of Small Language Models for Open Attribute-Value Extraction from Clinical Notes](https://mlsanity.com/paper/2507.01810/) (2025)
- [Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation Approach](https://mlsanity.com/paper/2410.22996/) (2024)
- [Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization](https://mlsanity.com/paper/2504.20125/) (2025)
- [ECG-LLM -- training and evaluation of domain-specific large language models for electrocardiography](https://mlsanity.com/paper/2510.18339/) (2025)
- [Large language model-enabled automated data extraction for concrete materials informatics](https://mlsanity.com/paper/2604.22938/) (2026)

## Abstract (arXiv)

The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling efficient extraction of structured, actionable data from unstructured text by non-experts. While applying LLMs to materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This review provides a comprehensive overview of LLM-based structured data extraction in materials science, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and materials science expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven materials research. The insights presented here could significantly enhance how researchers across disciplines access and utilize scientific information, potentially accelerating the development of novel materials for critical societal needs.