Recent Advances in Natural Language Processing in Chemistry and Materials Science

Natural Language Processing (NLP) in chemistry and materials science enables computers to understand, analyze, and generate human-readable output related to chemical concepts and materials. With the latest advancements in NLP, text processing at a near-human level has become possible in various tasks. Large Language Models (LLMs) have demonstrated exceptional proficiency in text generation, leading to the redefinition of numerous specific NLP tasks as text generation problems. This review explores the recent progress in applying LLMs to specialized domains, such as chemistry and materials science. It was discussed how LLMs overcome limitations of traditional NLP methods (such as rigid rule-based systems and shallow statistical models) by enabling context-aware interpretation of unstructured literature, flexible entity recognition (e.g., compounds, reactions), and generative tasks. Using the capabilities of LLMs, researchers in these fields can benefit from enhanced text processing, more accurate information extraction, and improved understanding of complex chemical concepts, making it a pivotal tool for accelerating discovery in chemically complex spaces, paving the way for novel tasks such as reaction prediction and molecular design.