# Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction

arXiv: https://arxiv.org/abs/2310.08383 · HTML: https://mlsanity.com/paper/2310.08383/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 85 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling](https://mlsanity.com/paper/2305.08264/) (2023)
- [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2006.03039/) (2020)
- [DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles](https://mlsanity.com/paper/2207.01079/) (2022)
- [Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing](https://mlsanity.com/paper/2101.01508/) (2021)

## Built on by (most cited first, 8 of 8)

- [Are LLMs Ready for Real-World Materials Discovery?](https://mlsanity.com/paper/2402.05200/) (2024)
- [MatExpert: Decomposing Materials Discovery by Mimicking Human Experts](https://mlsanity.com/paper/2410.21317/) (2024)
- [Foundational Large Language Models for Materials Research](https://mlsanity.com/paper/2412.09560/) (2024)
- [TritonDFT: Automating DFT with a Multi-Agent Framework](https://mlsanity.com/paper/2603.03372/) (2026)
- [LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature](https://mlsanity.com/paper/2510.26824/) (2025)
- [Extracting Polymer Nanocomposite Samples from Full-Length Documents](https://mlsanity.com/paper/2403.00260/) (2024)
- [Large language model-enabled automated data extraction for concrete materials informatics](https://mlsanity.com/paper/2604.22938/) (2026)
- [Adapt Semantics, Not Structure: Few-Instance Schema Calibration for Scientific PDF Extraction](https://mlsanity.com/paper/2609.34841/) (2026)

## Abstract (arXiv)

The discovery of new materials has a documented history of propelling human progress for centuries and more. The behaviour of a material is a function of its composition, structure, and properties, which further depend on its processing and testing conditions. Recent developments in deep learning and natural language processing have enabled information extraction at scale from published literature such as peer-reviewed publications, books, and patents. However, this information is spread in multiple formats, such as tables, text, and images, and with little or no uniformity in reporting style giving rise to several machine learning challenges. Here, we discuss, quantify, and document these challenges in automated information extraction (IE) from materials science literature towards the creation of a large materials science knowledge base. Specifically, we focus on IE from text and tables and outline several challenges with examples. We hope the present work inspires researchers to address the challenges in a coherent fashion, providing a fillip to IE towards developing a materials knowledge base.