大语言模型时代的材料科学：一种视角
Materials science in the era of large language models: a perspective
AI总结：
本文从理论、特性与案例出发，探讨大语言模型在材料科学中作为任务自动化与大规模知识提取工具的价值，主张将其定位为加速跨领域探索的辅助工作者而非洞见预言机。
摘要：
大语言模型（LLMs）因其出色的自然语言能力而引起了广泛关注，这些能力与各种涌现特性相结合，使其成为从复杂代码生成到组合问题启发式发现等工作流程中的多功能工具。在本文中，我们对其在材料科学研究中的适用性提出一种视角，认为它们在跨任务和跨学科中处理模糊需求的能力意味着它们可以成为帮助研究人员的强大工具。我们定性地考察了LLM的基础理论，将其与文献中的相关特性和技术联系起来，然后提供两个案例研究，展示它们在任务自动化和大规模知识提取中的应用。在目前的发展阶段，我们认为LLMs不应被视为新颖洞见的预言机，而更应被视为不知疲倦的工作者，能够加速和统一跨领域的探索。我们希望本文能让材料科学研究人员熟悉在自己的研究中使用这些工具所需的概念。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
