Beyond Text and Tables: Vision-Language Model Integration in ComProScanner for Extracting Materials Data from Scientific Figures with High Accuracy
arXiv:2606.00065v1 Announce Type: cross Abstract: Automated extraction of materials composition-property data from scientific literature has advanced considerably with the development of large language model-based pipelines; however, existing frameworks remain limited to textual and tabular content, overlooking the substantial proportion of quantitative property data reported exclusively in scientific figures. Here, we extend ComProScanner, a fully end-to-end multi-agent framework for automated composition-property database construction, with a native vision-language model (VLM) based figure e
The increasing sophistication of vision-language models and the demand for automated data extraction from scientific literature are converging to enable solutions beyond text. This development addresses a long-standing limitation in material science data collection.
This breakthrough significantly enhances the automated extraction of quantitative materials data, which is crucial for accelerating materials discovery and development through AI-driven research. It unlocks a vast amount of previously inaccessible data embedded in scientific figures.
The ability to accurately extract materials composition-property data directly from scientific figures using VLM integration in tools like ComProScanner eliminates a major bottleneck in creating comprehensive materials databases. This broadens the scope of automated scientific discovery pipelines.
- · Materials science researchers
- · AI/ML companies specializing in scientific data extraction
- · Pharmaceutical and chemical industries
- · Academic institutions
- · Manual data entry services for scientific figures
Automated material property databases will become significantly more comprehensive and faster to build, accelerating research.
This increased data availability will drive new discoveries in materials science, potentially leading to novel industrial applications.
The methodology could be generalized to other scientific disciplines, revolutionizing data extraction from visually rich scientific publications across fields.
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Read at arXiv cs.CL