Structure and thermodynamics of defects in Na-feldspar from a neural network potential

Abstract

The diffusive phase transformations occurring in feldspar, a common mineral in the crust of the Earth, are essential for reconstructing the thermal histories of magmatic and metamorphic rocks. Due to the long timescales over which these transformations proceed, the mechanism responsible for sodium diffusion and its possible anisotropy has remained a topic of debate. To elucidate this defect-controlled process, we have developed a Neural Network Potential (NNP) trained on first-principle calculations of Na-feldspar (Albite) and its charged defects. This new force field reproduces various experimentally known properties of feldspar, including its lattice parameters, elastic constants as well as heat capacity and DFT-calculated defect formation energies. A new type of dumbbell interstitial defect is found to be most favorable and its free energy of formation at finite temperature is calculated using thermodynamic integration. The necessity of including electrostatic corrections before training an NNP is demonstrated by predicting more consistent defect formation energies.

References in corpus (15)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• On-the-fly machine learning force field generation: Application to melting points
• Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
• Electrostatics-based finite-size correction for first-principles point defect calculations
• i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
• DynaPhoPy: A code for extracting phonon quasiparticles from molecular dynamics simulations
• Committee neural network potentials control generalization errors and enable active learning
• Uncertainty estimation for molecular dynamics and sampling
• Computing the Absolute Gibbs Free Energy in Atomistic Simulations: Applications to Defects in Solids
• A first-principles machine-learning force field for heterogeneous ice nucleation on microcline feldspar
• Spinney: post-processing of first-principles calculations of point defects in semiconductors with Python
• Modeling the Ga/As binary system across temperaturesand compositions from first principles
• Potassium self-diffusion in a K-rich single-crystal alkali feldspar
• Cation interstitial diffusion in lead telluride and cadmium telluride studied by means of neural network potential based molecular dynamics simulations