From Text to Insight: Large Language Models for Materials Science Data Extraction
This work addresses the challenge of accessing and utilizing scientific information for researchers across disciplines, but it is incremental as it synthesizes existing knowledge and presents frameworks rather than introducing new methods.
The paper tackles the problem of extracting structured data from unstructured natural language in materials science, which is crucial for systematic materials design, by reviewing how large language models (LLMs) can enable efficient extraction by non-experts, potentially accelerating the development of novel materials.
The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling efficient extraction of structured, actionable data from unstructured text by non-experts. While applying LLMs to materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This review provides a comprehensive overview of LLM-based structured data extraction in materials science, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and materials science expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven materials research. The insights presented here could significantly enhance how researchers across disciplines access and utilize scientific information, potentially accelerating the development of novel materials for critical societal needs.