arXiv:2604.22938v2 Announce Type: replace-cross Abstract: The promise of data-driven materials discovery remains constrained by the scarcity of large, high-quality, and accessible experimental datasets. Here, we introduce a generalizable large language model (LLM)-powered pipeline for automated extraction and structuring of materials data from unstructured scientific literature, using concrete materials as a representative and particularly challenging example. The pipeline exhibits robust performance across a broad range of LLMs and achieves an $F_1$ score of up to 0.98 for diverse composition
Advances in large language models coincident with increasing needs for data-driven materials discovery are enabling novel applications in automated scientific data extraction.
Automated data extraction from unstructured scientific literature significantly accelerates materials science R&D, overcoming a major bottleneck in data scarcity for novel material discovery.
The barrier to creating large, high-quality material datasets is substantially lowered, potentially speeding up innovation cycles in industries reliant on new materials.
- · Materials science researchers
- · AI/ML companies specializing in text extraction
- · Construction/Infrastructure sector
- · Manual data entry services
- · Traditional materials research methods
Faster identification and optimization of new materials due to improved data access.
Increased demand for specialized LLMs trained on scientific and technical literature, fostering a new niche in AI development.
The acceleration of sustainable and novel material development could address global challenges like climate change and resource scarcity more effectively.
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