Large language models in electronic laboratory notebooks: Transforming materials science research workflows
Mehrdad Jalali, Yi Luo, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Christof Wöll
Abstract
In recent years, there has been a surge in research efforts dedicated to harnessing the capabilities of Large Language Models (LLMs) in various domains, particularly in material science. This paper delves into the transformative role of LLMs within Electronic Laboratory Notebooks (ELNs) for scientific research. ELNs represent a pivotal technological advancement, providing a digital platform for researchers to record and manage their experiments, data, and findings. This study explores the potential of LLMs to revolutionize fundamental aspects of science, including experimental methodologies, data analysis, and knowledge extraction within the ELN framework. We present a demonstrative showcase of LLM applications in ELN environments and, furthermore, we conduct a series of empirical evaluations to critically assess the practical impact of LLMs in enhancing research processes within the dynamic field of materials science. Our findings illustrate how LLMs can significantly elevate the quality and efficiency of research outcomes in ELNs, thereby advancing knowledge and innovation in materials science research and beyond.