Radiation damage and phase stability of Al${}_x$CrCuFeNi${}_y$ alloys using a machine-learned interatomic potential

Abstract

We develop a machine-learned interatomic potential for AlCrCuFeNi high-entropy alloys (HEA) using a diverse set of structures from density functional theory calculated including magnetic effects. The potential is based on the computationally efficient tabulated version of the Gaussian approximation potential method (tabGAP) and is a general-purpose model for molecular dynamics simulation of the HEA system, with additional emphasis on radiation damage effects. We use the potential to study key properties of AlCrCuFeNi HEAs at different compositions, focusing on the FCC/BCC phase stability. Monte Carlo swapping simulations are performed to understand the stability and segregation of the HEA and reveal clear FeCr and Cu segregation. Close to equiatomic composition, a transition from FCC to BCC is detected, following the valence electron concentration stability rule. Furthermore, we perform overlapping cascade simulations to investigate radiation damage production and tolerance. Different alloy compositions show significant differences in defect concentrations, and all alloy compositions show enrichment of some elements in or around defects. We find that, generally, a lower Al content corresponds to lower defect concentrations during irradiation. Furthermore, clear short-range ordering is observed as a consequence of continued irradiation.

References in corpus (18)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Robust Structural Identification via Polyhedral Template Matching
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• Comparing molecules and solids across structural and alchemical space
• Machine-learning based interatomic potential for amorphous carbon
• Machine learning a general purpose interatomic potential for silicon
• A scalable parallel Monte Carlo algorithm for atomistic simulations of precipitation in alloys
• Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
• General-purpose machine-learned potential for 16 elemental metals and their alloys
• Machine-learning interatomic potential for radiation damage and defects in tungsten
• Improving the accuracy of the neuroevolution machine learning potential for multi-component systems
• Local segregation versus irradiation effects in high-entropy alloys: Steady-state conditions in a driven system
• Simple machine-learned interatomic potentials for complex alloys
• Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics
• Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
• Development of an Atomic Cluster Expansion potential for iron and its oxides
• A collinear-spin machine learned interatomic potential for Fe${}_7$Cr${}_2$Ni alloy