| Abstract Scope |
Since the advent of ChatGPT in November 2022, the field of large language models (LLMs) has seen an explosive development. In the scientific community, LLMs have been employed to automate literature reviews, assist data analysis, and generate hypotheses. In the materials science community, there is a rapid surge in the interest in LLMs because the majority of information concerning materials exists as text, aligning closely with the text-centric nature of LLMs. In this work, we explore the abilities of LLMs for two tasks: information extraction and material design. In the first task, we employed an LLM to extract information including chemical compositions, processing conditions, microstructures, and properties. The LLM was shown to significantly outperform a conventional rule-based method. In the second task, we utilized an LLM to establish the relationship between structures and properties in a couple cases. The LLM-based relationship was compared favorably with machine-learning based ones. |