Materials science in the era of large language models: a perspective
This perspective aims to help materials science researchers leverage LLMs for workflow acceleration, but it is incremental as it applies existing LLM methods to a new domain without introducing new techniques.
The paper explores the applicability of large language models (LLMs) to materials science research, arguing they can accelerate tasks like automation and knowledge extraction, though they should be viewed as tools for assistance rather than sources of novel insight.
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.