Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach

A novel approach for the analysis of extended X-ray absorption fine structure (EXAFS) spectra is developed exploiting an inverse machine learning-based algorithm. Through this approach, it possible to explore and account for, in a precise way, nonlinear geometry dependence photoelectron backscattering phases amplitudes single multiple scattering paths. In addition, determined parameters are directly related 3D atomic structure, without need use complex parametrization as classical fitting approach. The applicability its potential advantages over fit were demonstrated by EXAFS data two molecular systems, namely, KAu (CN)2 [RuCl2(CO)3]2 complexes.