Cation interstitial diffusion in lead telluride and cadmium telluride studied by means of neural network potential based molecular dynamics simulations

TL;DR

This study uses neural network potential-based molecular dynamics to investigate cation interstitial diffusion mechanisms and activation energies in lead telluride and cadmium telluride crystals, revealing complex temperature-dependent behaviour.

Contribution

It introduces a neural network potential approach for simulating cation diffusion in PbTe and CdTe, providing detailed insights into diffusion mechanisms and activation energies.

Findings

Interstitials migrate via hopping and exchange mechanisms.

Activation energies for diffusion mechanisms are quantified.

Diffusion coefficients show deviation from Arrhenius behaviour.

Abstract

Using a recently developed approach to represent ab initio based force fields by a neural network potential, we perform molecular dynamics simulations of lead telluride (PbTe) and cadmium telluride (CdTe) crystals. In particular, we study the diffusion of a single cation interstitial in these two systems. Our simulations indicate that the interstitials migrate via two distinct mechanisms: through hops between interstitial sites and through exchanges with lattice atoms. We extract activation energies for both of these mechanisms and show how the temperature dependence of the total diffusion coefficient deviates from Arrhenius behaviour. The accuracy of the neural network approach is estimated by comparing the results for three different independently trained potentials.