Materials science in the era of large language models: a perspective

TL;DR

This paper explores how large language models can be applied to materials science, highlighting their potential to automate tasks and extract knowledge, thereby accelerating research and fostering interdisciplinary collaboration.

Contribution

It provides a perspective on LLM theory, connects it to materials science applications, and presents case studies demonstrating their practical utility in the field.

Findings

LLMs can automate complex tasks in materials science.

They enable large-scale knowledge extraction.

LLMs serve as tools to accelerate and unify research efforts.

Abstract

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.