Predicting X-Ray Absorption Spectra of Materials Using Graph-based Neural Networks

X-ray absorption spectroscopy (XAS) is an important characterization technique in condensed matter physics, materials science and chemistry for resolving local structural and electronic properties. First-principles simulations of XAS have played a critical role in interpreting the otherwise abstract spectral features and obtaining physical insight. However, the widely used simulation methods are computationally expensive, especially when computing the near edge region. Machine learning (ML) models trained from first-principles simulation data have been widely employed on a variety of problems to speed up inference while maintaining high fidelity. However, to the best of our knowledge, ML has not been applied to predicting the XAS for periodic materials. In this work, we use the graph-based neural networks to predict the X-ray absorption near-edge structure (XANES) spectra of a wide range of materials from the Materials Project. Our models are accurate and highly efficient, and therefore can enable high-throughput spectrum sampling in the large materials phase space, which could allow for efficient structure refinement and may offer new routes for real time spectral interpretation in autonomous experimentation.