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.

翻译：由于已发表研究论文呈指数级增长，科学家已无法阅读所有文献，即使在其自身研究领域内亦如此。本研究探索利用大型语言模型（LLMs）从材料科学领域的学术摘要中提取核心概念与语义信息，以发现人类尚未注意到的关联，从而启发近期/中期未来研究方向。我们证明LLMs能比自动关键词提取方法更高效地构建概念图谱作为科学文献的抽象表征。基于历史数据训练机器学习模型以预测新兴概念组合（即新研究构想）。研究表明整合语义概念信息可提升预测性能。通过与领域专家就个性化模型建议进行定性访谈，验证了本模型的适用性。模型通过预测尚未被研究的前沿主题组合，能够有效激发材料科学家在创造性思维过程中的灵感。