  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.
