Abstract
As interest in Cerium containing alloys and Al-Ce alloys in particular grows, the need to generate predictions of mechanical, thermodynamic and transport properties across a compositional space becomes more pronounced. The absence of a reliable interaction potential to be used in classical molecular dynamics (MD) simulation of γ-Ce is a fundamental bottleneck to subsequent alloy simulation. In this work, a Modified Embedded Atom Method (MEAM) potential to be used in MD simulation for γ-Ce has been generated. The parameterization process involved two steps. First, the iterative Latin hypercube sampling (LHS) method was used to generate MEAM parameter sets based on an automated optimization of energies and forces relative to a training set of small configurations evaluated through Density Functional Theory (DFT). Second, a human-guided optimization in that local parameter space was performed to refine the parameters to satisfy an array of mechanical, thermodynamic and transport properties available from the experimental literature. When used in an MD simulation, the resulting potential provides excellent estimates of all tested material properties across a broad temperature range. This γ-Ce potential, when combined with existing MEAM potential for Al, will form the necessary foundation for the subsequent development of a mixture potential, enabling the simulation of Al-Ce and Al-Ce-X alloys.
Original language | English |
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Article number | 112454 |
Journal | Computational Materials Science |
Volume | 230 |
DOIs | |
State | Published - Oct 25 2023 |
Externally published | Yes |
Funding
This work was supported by the Critical Materials Institute, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Materials and Manufacturing Technologies . The authors also thank the Institute for Advanced Materials & Manufacturing (IIAM) located at the University of Tennessee (UTK), Knoxville for the materials characterization facilities used for XRD and CTE. This work used the computational resources of Lawrence Livermore National Laboratory’s Quartz and Ruby supercomputers as well as the computational resources of Infrastructure for Scientific Applications and Advanced Computing (ISAAC) at The University of Tennessee, Knoxville. The authors also thank Vince Lordi for support in accessing and running simulations on Livermore computing resources. This work was supported by the Critical Materials Institute, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Materials and Manufacturing Technologies. The authors also thank the Institute for Advanced Materials & Manufacturing (IIAM) located at the University of Tennessee (UTK), Knoxville for the materials characterization facilities used for XRD and CTE. This work used the computational resources of Lawrence Livermore National Laboratory's Quartz and Ruby supercomputers as well as the computational resources of Infrastructure for Scientific Applications and Advanced Computing (ISAAC) at The University of Tennessee, Knoxville. The authors also thank Vince Lordi for support in accessing and running simulations on Livermore computing resources.
Funders | Funder number |
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Critical Materials Institute | |
Institute for Advanced Materials & Manufacturing | |
U.S. Department of Energy | |
Office of Energy Efficiency and Renewable Energy | |
University of Tennessee | |
University of Tennessee, Knoxville | |
Advanced Materials and Manufacturing Technologies Office |
Keywords
- Alloy
- Cerium
- Density functional theory
- Machine learning
- Modified embedded atom method
- Molecular dynamics