Enhancing UV Spectral Prediction through Auxiliary Task, Curriculum Learning, and Curvature Limitation

Accurate UV spectral prediction is challenging for machine learning. UV spectra exhibit broad absorption bands characterized by the peak positions, band shapes, and curvature profiles. However, current models fail to capture these characteristics. We present Peak Position Awareness (PPA), Curriculum Learning for Interpolated Abstracted Spectra (CLIAS), and Spectrum Curvature Limitation (SCL) to handle the above characteristics, showing consistent improvements over diverse models.