A vast amount of scientific knowledge is embedded in journal articles as unstructured text, creating challenges for efficiently extracting detailed insights. Traditionally, expert-authored reviews summarize research progress, but they often struggle to capture the intricate synthesis protocols in individual papers and provide limited quantitative comparisons of experimental techniques. Recent advancements in machine learning, particularly natural language processing (NLP), have enabled automated text mining and information extraction. However, in materials science, most approaches have focused on refining model architectures rather than addressing domain-specific challenges such as data annotation and the extraction of complex synthesis details. We present a machine learning framework for extracting synthesis protocols of 2D materials, including graphene and TMDs, from publications spanning 1980-2022. By combining named entity recognition (NER) and extractive question answering (EQA), we retrieve both categorical and numerical synthesis parameters. Generative models are further used to summarize and generate experimental recipes, enabling knowledge transfer across material systems. Our domain-specific, fine-tuned models offer improved precision and interpretability compared to general-purpose approaches. This scalable framework helps unlock hidden insights from literature, supporting data-driven synthesis optimization and accelerating materials discovery.
Lu et al. (Thu,) studied this question.