Local lattice dynamics of hcp zinc from EXAFS and machine-learning interatomic potentials

Abstract

The lattice dynamics of hexagonal close-packed (hcp) zinc, a prototypical anisotropic metal, is studied using temperature-dependent Zn K-edge extended X-ray absorption fine structure (EXAFS) spectroscopy combined with atomistic simulations. The reverse Monte Carlo method enable the extraction of mean-square relative displacements (MSRDs) for eight coordination shells, providing a shell-resolved description of thermal motion. The MSRD temperature dependence, analysed using the correlated Einstein model, yields effective interatomic force constants and reveals pronounced anisotropy between in-plane and out-of-plane interactions. This anisotropy is further quantified by the ratio of MSRDs for the first and second coordination shells, which closely matches the anisotropic displacement parameters from diffraction experiments. Molecular dynamics simulations using the CHGNet universal machine-learning interatomic potential show that the original model overestimates thermal disorder, while a fine-tuned version substantially improves agreement with experimental EXAFS spectrum and radial distribution function. Overall, EXAFS-informed analysis is effective for validating and refining machine-learning interatomic potentials.

References in corpus (5)

• Lattice dynamics and correlated atomic motion from the atomic pair distribution function
• Treatment of disorder effects in X-ray absorption spectra beyond the conventional approach
• Advanced approach to the local structure reconstruction and theory validation on the example of the W L${}_3$-edge extended X-ray absorption fine structure of tungsten
• Peculiarities of the local structure in new medium- and high-entropy, low-symmetry tungstates
• Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2