Solute Segregation in Polycrystalline Aluminum From Hybrid Monte Carlo and Molecular Dynamics Simulations With a Unified Neuroevolution Potential

Abstract

The fundamental mechanisms of solute segregation and their impacts on material properties remain elusive, primarily due to the complexity and computational challenges in modeling. To address this, we present a specialized GPU implementation of highly efficient hybrid Monte Carlo and molecular dynamics (MCMD) algorithms in the open-source GPUMD package. Using this efficient MCMD approach, combined with a general-purpose machine-learning-based neuroevolution potential for 16 elemental metals and their alloys, we simulate the segregation of 15 solutes in polycrystalline Al. Our results reveal distinct segregation patterns for these solutes (Ag, Al, Au, Cr, Cu, Mg, Mo, Ni, Pb, Pd, Pt, Ta, Ti, V, W, Zr) in polycrystalline Al. We further investigate the impact of solutes on the strength of polycrystalline Al, analyzing the mechanisms of solute strengthening and embrittlement at the atomistic level. Our findings indicate the critical roles of grain boundaries cohesion and the nucleation and movement of Shockley dislocations in determining the material's strength. We anticipate that our efficient GPU-accelerated MCMD implementation in GPUMD, along with the insights into solute segregation behavior in polycrystalline Al, will be valuable for the design of Al alloys and other multi-component materials, including medium-entropy materials, high-entropy materials, and complex concentrated alloys.

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