Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature
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 bat...
| Authors | Tanjin He, Aikaterini Vriza, Logan Ward et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 26 July 2026 |
| Last updated at source | 31 July 2026 |
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