Mechanism and kinetics of sodium diffusion in Na-feldspar from neural network based atomistic simulations

TL;DR

This study uses neural network-based atomistic simulations to investigate sodium diffusion mechanisms in Na-feldspar, providing insights into diffusion coefficients, mechanisms, and anisotropy relevant for geological thermal history reconstructions.

Contribution

It introduces a neural network potential for molecular dynamics simulations of sodium feldspar, revealing detailed diffusion mechanisms and anisotropy.

Findings

Neural network simulations agree with experimental diffusion coefficients.

Identified a dumbbell-shaped alkali defect as key to diffusion.

Explained diffusion anisotropy through defect orientation effects.

Abstract

Alkali diffusion is a first-order control for microstructure and compositional evolution of feldspar during cooling from high temperatures of primary magmatic or metamorphic crystallization, and knowledge of the respective diffusion coefficients is crucial for reconstructing thermal histories. Our understanding of alkali diffusion in feldspar is, however, hindered by an insufficient grasp of the underlying diffusion mechanisms. We performed molecular dynamics simulations of sodium feldspar (Albite) containing different point defects using a recently developed neural network potential. A high degree of agreement between the sodium self-diffusion coefficients obtained from model simulations and those determined experimentally in earlier studies motivated a detailed investigation into the interstitial and vacancy mechanisms, corresponding jump rates, correlation factors and anisotropy. We identified a dumbbell shaped double occupancy of an alkali site as an important point defect and a correlation effect originating from the orientation of the dumbbell as a possible cause for the $\perp\!\!(001) > \, \perp\!\!(010)$ diffusion anisotropy, which has been reported in a slew of feldspar cation diffusion experiments.