Predicting new research directions in materials science using large language models and concept graphs

Due to an exponential increase in published research articles, it is impossible for individual scientists to read all publications, even within their own research field. Here we investigate the use of large language models to extract the main concepts and semantic information from scientific abstracts in the domain of materials science to identify links that were not noticed by humans and to suggest inspiring near and/or mid-term future research directions. We show that large language models can extract concepts more efficiently than automated keyword extraction methods to build a concept graph as an abstraction of the scientific literature. A machine learning model is trained to predict emerging combinations of concepts, that is, new research ideas, based on historical data. We demonstrate that integrating semantic concept information leads to increased prediction performance. The applicability of our model is demonstrated in qualitative interviews with domain experts based on individualized model suggestions. We show that the model can inspire materials scientists in their creative thinking process by predicting innovative combinations of concepts that have not yet been investigated.