Predicting New Research Directions in Materials Science using Large Language Models and Concept Graphs
This work addresses the challenge for materials scientists in keeping up with vast literature by suggesting incremental improvements in predicting new research ideas.
The authors tackled the problem of information overload in materials science by using large language models to extract concepts from abstracts and predict novel research directions, demonstrating improved prediction performance through semantic integration and inspiring domain experts with innovative topic combinations.
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. In this work, we investigate the use of large language models (LLMs) for the purpose of extracting the main concepts and semantic information from scientific abstracts in the domain of materials science to find links that were not noticed by humans and thus to suggest inspiring near/mid-term future research directions. We show that LLMs 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, i.e. new research ideas, based on historical data. We demonstrate that integrating semantic concept information leads to an 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 topics that have not yet been investigated.