Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Abstract

Large language models (LLMs) are creating a new paradigm for materials science by transforming textual insights into experimental findings. Leveraging their strengths in natural language understanding, multimodal alignment, and few‐shot reasoning, LLMs already show potential in property prediction, synthesis planning, and uncertainty quantification. This perspective highlights four key roles, Oracle, Surrogate, Quant, and Arbiter, to systematize recent advancements of LLMs in knowledge extraction, property inference, risk assessment, and decision‐making. Experience suggests that true value arises from integrating these capabilities into a verifiable, traceable loop rather than merely scaling model size. However, LLMs still face challenges due to data heterogeneity, limited interpretability, hallucination control, and misalignment with scientific tasks. To address these issues, we propose three forward‐looking directions: developing domain‐adapted foundation models infused with materials science context, establishing a standardized cross‐modal data infrastructure, and incorporating expert feedback alongside robotic automated experimentation into a fully traceable research loop. Through enhanced human–AI collaboration and methodological innovation, LLMs can transform from general‐purpose language tools into scientifically aware partners, advancing materials discovery toward a more efficient, interpretable, and sustainable future.

Topics & Concepts