  The advent of massive data repositories has propelled machine learning
techniques to the front lines of many scientific fields, and exploring new
frontiers by leveraging the predictive power of machine learning will greatly
accelerate big data-assisted discovery. In this work, we show that graph-based
neural networks can be used to predict the near edge x-ray absorption structure
spectra of molecules with exceptional accuracy. The predicted spectra reproduce
nearly all the prominent peaks, with 90% of the predicted peak locations within
1 eV of the ground truth. Our study demonstrates that machine learning models
can achieve practically the same accuracy as first-principles calculations in
predicting complex physical quantities, such as spectral functions, but at a
fraction of the cost.
