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.

翻译：大语言模型因其出色的自然语言处理能力而引起了广泛关注，结合各种涌现特性，它们已成为从复杂代码生成到组合问题启发式求解等工作流程中的多功能工具。本文从视角上探讨了其在材料科学研究中的适用性，认为它们处理跨任务和跨学科模糊需求的能力，使其成为辅助研究人员的强大工具。我们定性分析了大语言模型的基础理论，将其与文献中的相关特性和技术联系起来，随后通过两个案例研究展示了它们在任务自动化和大规模知识提取中的应用。我们认为，在目前的发展阶段，大语言模型更应被视为能够加速并统一跨领域探索的不知疲倦的工作者，而非产生新颖见解的预言者。希望本文能让材料科学研究人员熟悉利用这些工具所需的概念，并将其应用于自己的研究中。