  Advancements in machine learning and artificial intelligence are transforming
materials discovery. Yet, the availability of structured experimental data
remains a bottleneck. The vast corpus of scientific literature presents a
valuable and rich resource of such data. However, manual dataset creation from
these resources is challenging due to issues in maintaining quality and
consistency, scalability limitations, and the risk of human error and bias.
Therefore, in this work, we develop a chemist AI agent, powered by large
language models (LLMs), to overcome these challenges by autonomously creating
structured datasets from natural language text, ranging from sentences and
paragraphs to extensive scientific research articles. Our chemist AI agent,
Eunomia, can plan and execute actions by leveraging the existing knowledge from
decades of scientific research articles, scientists, the Internet and other
tools altogether. We benchmark the performance of our approach in three
different information extraction tasks with various levels of complexity,
including solid-state impurity doping, metal-organic framework (MOF) chemical
formula, and property relations. Our results demonstrate that our zero-shot
agent, with the appropriate tools, is capable of attaining performance that is
either superior or comparable to the state-of-the-art fine-tuned materials
information extraction methods. This approach simplifies compilation of machine
learning-ready datasets for various materials discovery applications, and
significantly ease the accessibility of advanced natural language processing
tools for novice users in natural language. The methodology in this work is
developed as an open-source software on https://github.com/AI4ChemS/Eunomia.
