OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a powerful technique for probing local chemical environments. However, interpreting XAS spectra remains challenging, due to high computational costs and the need for domain expertise. To overcome these barriers, we introduce OmniXAS, a graph neural network framework that leverages transfer learning to directly predict XAS spectra from atomic structures. By capturing the shared spectral trends across the 3d transition metal family, OmniXAS learns a universal model that can be effectively fine-tuned on each specific element. OmniXAS achieves high predictive accuracy as demonstrated on the K-edge spectra of eight 3d transition metals (Ti--Cu), enabling real-time prediction of XAS spectra with minimal computational overhead.