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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Recent Investigations and Developments of Titanium-containing High Entropy Alloys
Presentation Title Neural-Network based atomistic simulation on chemical ordering formation and its kinetics in medium entropy alloys
Author(s) Shigenobu Ogata, Jun-ping Du
On-Site Speaker (Planned) Shigenobu Ogata
Abstract Scope Multi-principal alloys (MPAs), such as medium entropy alloys and high entropy alloys, are a breakthrough from the traditional alloy containing a single principal element. Recently, chemical ordering in MPAs is attracting a lot of attention. However, the concrete atomic configuration of the chemical ordering and the relationship between the chemical ordering and the annealing temperature and time has not been fully clarified. Here, we studied the chemical ordering in CrCoNi MEA using a large-scaled atomic model with a neural network potential (NNP) trained by density functional theory and molecular dynamics/Monte-Carlo (MD/MC) hybrid simulated annealing method. In addition, the kinetics of chemical ordering evolution through vacancy diffusion is studied using the NNP with kinetic Monte-Carlo (kMC) simulation method combined with a novel neural network technique of fast activation energy evaluation. The kMC simulations at various temperatures lead to a diagram of Time-Temperature-Chemical ordering (TTC) relation.
Proceedings Inclusion? Planned: None Selected
Keywords High-Entropy Alloys, Modeling and Simulation, Computational Materials Science & Engineering


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