Large Language Models (LLMs) have garnered considerable interest due to their
impressive natural language capabilities, which in conjunction with various
emergent properties make them versatile tools in workflows ranging from complex
code generation to heuristic finding for combinatorial problems. In this paper
we offer a perspective on their applicability to materials science research,
arguing their ability to handle ambiguous requirements across a range of tasks
and disciplines mean they could be a powerful tool to aid researchers. We
qualitatively examine basic LLM theory, connecting it to relevant properties
and techniques in the literature before providing two case studies that
demonstrate their use in task automation and knowledge extraction at-scale. At
their current stage of development, we argue LLMs should be viewed less as
oracles of novel insight, and more as tireless workers that can accelerate and
unify exploration across domains. It is our hope that this paper can
familiarise material science researchers with the concepts needed to leverage
these tools in their own research.

我们提供一个对大型语言模型在材料科学研究中的适用性的观点，认为它们处理一系列任务和学科中的模糊需求的能力使它们成为帮助研究人员的强大工具。我们定性地研究了基础的大型语言模型理论，并将其与文献中的相关性质和技术联系起来，然后提供了两个案例研究，展示了它们在大规模任务自动化和知识提取中的应用。我们认为，在当前的发展阶段，大型语言模型应该被视为能够加速和统一领域探索的不知疲倦的工具，而不是新见解的神谕。希望本文能让材料科学研究人员了解到运用这些工具的相关概念。