Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential
arxiv(2024)
摘要
One of the most effective methods to enhance the strength of aluminum alloys
involves modifying grain boundaries (GBs) through solute segregation. However,
the fundamental mechanisms of solute segregation and their impacts on material
properties remain elusive. In this study, we implemented highly efficient
hybrid Monte Carlo and molecular dynamics (MCMD) algorithms in the graphics
process units molecular dynamics (GPUMD) package. Using this efficient MCMD
approach combined with a general-purpose machine-learning-based neuroevolution
potential (NEP) for 16 elemental metals and their alloys, we simulated the
segregation of 15 solutes in polycrystalline Al. Our results elucidate the
segregation behavior and trends of 15 solutes in polycrystalline Al.
Additionally, we investigated the impact of solutes on the strength of
polycrystalline Al. The mechanisms underlying solute strengthening and
embrittlement were analyzed at the atomistic level, revealing the importance of
GB cohesion, as well as the nucleation and movement of Shockley dislocations,
in determining the material's strength. We anticipate that our developed
methods, along with our 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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