Machine learning-accelerated small-angle X-ray scattering analysis of disordered two- and three-phase materials

Dataset and code used in M Röding, et al, "Machine learning-accelerated small-angle X-ray scattering analysis of disordered two- and three-phase materials", published in Frontiers in Materials. In this work, we develop a machine learning-based framework for prediction of material parameters from small-angle X-ray scattering (SAXS) data. The method is trained using data from a Gaussian random field-based model for the electron density of the material and a very fast Fourier transform-based numerical method for simulating realistic SAXS measurements. The prediction is performed using regression with XGBoost. Herein, the codes in Matlab and Python/XGBoost necessary to investigate the prediction models and reproduce the results of the paper are supplied. Also, the dataset and the trained XGBoost models are supplied.