# Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature

arXiv: https://arxiv.org/abs/2607.23886 · HTML: https://mlsanity.com/paper/2607.23886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 26 Jul 2026.

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

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- [Extracting Scientific Figures with Distantly Supervised Neural Networks](https://mlsanity.com/paper/1804.02445/) (2018)
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- [RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing](https://mlsanity.com/paper/2305.11845/) (2023)
- [Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature](https://mlsanity.com/paper/2302.02303/) (2023)
- [A Two-stage Framework for Compound Figure Separation](https://mlsanity.com/paper/2101.09903/) (2021)
- [Agentic LLM Reasoning in a Self-Driving Laboratory for Air-Sensitive Lithium Halide Spinel Conductors](https://mlsanity.com/paper/2604.11957/) (2026)
- [Plot2Spectra: an Automatic Spectra Extraction Tool](https://mlsanity.com/paper/2107.02827/) (2021)
- ["DIVE" into Hydrogen Storage Materials Discovery with AI Agents](https://mlsanity.com/paper/2508.13251/) (2025)
- [XASDB -- Design and Implementation of an Open-Access Spectral Database](https://mlsanity.com/paper/2509.13566/) (2025)

## Abstract (arXiv)

X-ray absorption spectroscopy (XAS) is central to understanding the local electronic and atomic structure of materials, yet most published spectra remain inaccessible to data-driven analysis because they are embedded in figures and described through fragmented textual context in the literature. Here, we use multimodal (image and text) literature mining to transform this dispersed knowledge into an AI-ready experimental data resource. We developed a scalable spectroscopy data digitization pipeline that identifies XAS figures in full-text articles, digitizes spectral curves, and links each spectrum to accompanying metadata on the measured edge and material. Applying this pipeline to the battery literature produced an open dataset of 13,740 XAS spectra, spanning 66 absorbing elements and diverse battery chemistries, with expert validation confirming accurate extraction of spectral and metadata information. By converting literature-embedded spectra into structured numerical data, this dataset provides a foundation for large-scale XAS analysis, cross-laboratory comparison, high-throughput characterization, and autonomous discovery of advanced materials.