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<p>Large language models (LLMs) create exciting possibilities to accelerate scientific discovery and knowledge dissemination in materials science. While LLMs have been successfully applied to select scientific problems and rudimentary challenges, they currently fall short of being practical materials science tools. In this Perspective, we show relevant failure cases of LLMs in materials science that reveal the current limitations of LLMs related to comprehending and reasoning over complex, interconnected materials science knowledge. Given these shortcomings, we outline a framework for developing materials science LLMs (MatSci-LLMs) that are grounded in domain knowledge, which can enable hypothesis generation followed by hypothesis testing for impactful materials science challenges. The path to attaining performant MatSci-LLMs rests, in large part, on building high-quality, multimodal datasets sourced from scientific literature, where various information extraction challenges persist. As such, we describe key materials science information extraction challenges that need to be overcome to build large-scale, multimodal datasets that capture valuable materials science principles and broader knowledge.</p>

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Enabling large language models for real-world materials discovery

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Santiago Miret,
  • N. M. Anoop Krishnan

摘要

Large language models (LLMs) create exciting possibilities to accelerate scientific discovery and knowledge dissemination in materials science. While LLMs have been successfully applied to select scientific problems and rudimentary challenges, they currently fall short of being practical materials science tools. In this Perspective, we show relevant failure cases of LLMs in materials science that reveal the current limitations of LLMs related to comprehending and reasoning over complex, interconnected materials science knowledge. Given these shortcomings, we outline a framework for developing materials science LLMs (MatSci-LLMs) that are grounded in domain knowledge, which can enable hypothesis generation followed by hypothesis testing for impactful materials science challenges. The path to attaining performant MatSci-LLMs rests, in large part, on building high-quality, multimodal datasets sourced from scientific literature, where various information extraction challenges persist. As such, we describe key materials science information extraction challenges that need to be overcome to build large-scale, multimodal datasets that capture valuable materials science principles and broader knowledge.