  Machine-learning potentials for materials, namely the moment tensor
potentials (MTPs), were validated using experimental EXAFS spectra for the
first time. The MTPs for four metals (bcc W and Mo, fcc Cu and Ni) were
obtained by the active learning algorithm of fitting to the results of the
calculations using density functional theory (DFT). The MTP accuracy was
assessed by comparing metal K-edge EXAFS spectra obtained experimentally and
computed from the results of molecular dynamics (MD) simulations. The
sensitivity of the method to various aspects of the MD and DFT models was
demonstrated using Ni as an example. Good agreement was found for W, Mo and Cu
using the recommended PAW pseudopotentials, whereas a more accurate
pseudopotential with 18 valence electrons was required for Ni to achieve a
similar agreement. The use of EXAFS spectra allows one to estimate the MTP
ability in reproducing both average and dynamic atomic structures.
