# MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials Discover

arXiv: https://arxiv.org/abs/2311.06303 · HTML: https://mlsanity.com/paper/2311.06303/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)

## Built on by (most cited first, 4 of 4)

- [Towards Agentic Intelligence for Materials Science](https://mlsanity.com/paper/2602.00169/) (2026)
- [Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts](https://mlsanity.com/paper/2505.21646/) (2025)
- [From Word2Vec to Transformers: Text-Derived Composition Embeddings for Filtering Combinatorial Electrocatalysts](https://mlsanity.com/paper/2603.08881/) (2026)
- [Knowledge-Driven XRD Phase Identification via Multi-View Retrieval and Explanation](https://mlsanity.com/paper/2609.31888/) (2026)

## Abstract (arXiv)

MatNexus is a specialized software for the automated collection, processing, and analysis of text from scientific articles. Through an integrated suite of modules, the MatNexus facilitates the retrieval of scientific articles, processes textual data for insights, generates vector representations suitable for machine learning, and offers visualization capabilities for word embeddings. With the vast volume of scientific publications, MatNexus stands out as an end-to-end tool for researchers aiming to gain insights from scientific literature in material science, making the exploration of materials, such as the electrocatalyst examples we show here, efficient and insightful.