MembraneBERT: a domain-tailored NLP model for membrane-based gas separation data extraction

Membrane-based separation is a critical technology for energy and environmental processes, positioning it at the forefront of materials science research. To facilitate the data-driven advances in this field, we developed an integrated natural language processing pipeline including a domain-tailored transformer model for literature mining. One major challenge is data imbalance which biases the model toward majority classes and leads to overfitting on dominant patterns. We introduced two complementary strategies to direct the model’s attention toward minority classes: (1) a data augmentation method that prevents data leakage, and (2) model fine-tuning using a customized loss function that integrates class-weighted cross-entropy and focal loss. The resulting model MembraneBERT achieves an average Precision of 0.906, Recall of 0.796, and F1-score of 0.844, respectively. Applied to 3,580 additional articles, the model extracted 2,283 structured entries on membranes and their gas separation performance, offering a practical framework for AI-driven extraction of membrane performance data to support subsequent data-driven research.