Literature Screening and Knowledge Extraction via Domain-Adaptive Transfer Learning with Large Language Models

The rapid growth of scientific literature poses major challenges for large-scale knowledge extraction in materials science, particularly when domain-specific concepts are expressed ambiguously or require deep contextual reasoning. Here, we propose a workflow that integrates domain-adaptive transfer learning, knowledge distillation, and reinforcement learning for high-precision literature screening and structured knowledge extraction in a ferroelectric Curie-temperature case study. A large open-source language model is used as a teacher to generate high-fidelity pseudolabels from full-text articles, which are then employed to train a compact student model via reinforcement-learning-based optimization. Under the same title-and-abstract input condition, the student model achieves 93.6% classification accuracy, exceeding the zero-shot Qwen3-235B-A22B baseline by 7.1 percentage points, while reducing parameter count by 138-fold and inference time by approximately 50-fold relative to the full-text teacher pipeline. This study clarifies the contextual ambiguity of “Curie temperature” in ferroelectric versus ferromagnetic literature and supports the construction of a verified ferroelectric materials knowledge base from the studied corpus. 5-fold cross-validation and ablation studies confirm the effectiveness of the proposed workflow for this task. These results demonstrate a task-specific route for combining full-text pseudolabeling with lightweight title-and-abstract screening while retaining full-text verification before database construction.