  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.
