  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.
