Enhancing Property Prediction of OLED Materials under Data Scarcity via Cognitive Eye-Tracking-Informed Attention Mechanism Models
This paper presents a deep learning framework that enhances property prediction for OLED materials under data scarcity by integrating human eye-tracking data to construct an interpretable, human-mimicking attention mechanism capable of achieving near-DFT accuracy in predicting molecular orbital distributions.