Evaluation of Structural Properties and Defect Energetics in Al$_x$Ga$_{1-x}$N Alloys

TL;DR

This study uses a machine learning interatomic potential to explore how alloy composition affects defect energetics and structural properties in AlGaN alloys, providing insights for material optimization.

Contribution

The paper introduces a MLIP-based approach to accurately model defect energetics in disordered AlGaN alloys, overcoming limitations of traditional first-principles methods.

Findings

Nitrogen Frenkel pair formation energies are highly sensitive to local chemical environment.

Ga and Al vacancy migration energies are relatively insensitive to alloy composition.

Interstitial migration energies show strong dependence on alloying in AlGaN.

Abstract

Al$_x$Ga$_{1-x}$N alloys are essential for high-performance optoelectronic and power devices, yet the role of composition on defect energetics remains underexplored, largely due to the limitations of first-principles methods in modeling disordered alloys. To address this, we employ a machine learning interatomic potential (MLIP) to investigate the structural and defect-related physical properties in Al$_x$Ga$_{1-x}$N. The MLIP is first validated by reproducing the equation of state, lattice constants, and elastic constants of the binary endpoints, GaN and AlN, as well as known defect formation and migration energies from density functional theory and empirical potentials. We then apply the MLIP to evaluate elastic constants of AlGaN alloys, which reveals a non-linear relation with alloying effect. Our results reveal that nitrogen Frenkel pair formation energies and the migration barriers for nitrogen point defects are highly sensitive to the local chemical environment and migration path. In contrast, Ga and Al vacancy migration energies remain relatively insensitive to alloy composition, whereas their interstitial migration energies exhibit stronger compositional dependence. These results provide quantitative insight into how alloying influences defect energetics in AlGaN, informing defect engineering strategies for improved material performance.