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Machine-learned potentials construction for high-entropy alloys properties using GPUMD - UVA School of Engineering and Applied Science

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GPUMD-UVA (In-progress)

Research Focus

The project, led by Professor Keivan Esfarjani, with Bikash Timalsina as the graduate student, focuses on investigating the thermal properties of high-entropy alloys (HEAs) using molecular dynamics (MD) simulations.

Tools and Methods

  • Utilized the GPUMD package, Python, MATLAB, and Linux commands for simulation tasks.
  • GPUMD package provided efficient MD simulation capabilities.
  • Python and MATLAB facilitated data analysis and visualization.
  • Linux commands were used to manage and execute simulations on the UVA Rivanna supercomputer.

Simulation Tasks

  • Conducted over 30 thermal simulations for MgNiO and HEO alloys.
  • Optimized simulations with various combinations of driving forces and runtimes for each alloy.
  • Repeated the process for different temperature ranges.

Visualization and Analysis

  • Generated numerous graphs for each simulation of different alloys at different temperature to visualize thermal conductivity and spectral heat currents over the production time.

Simulation Results and Discussions

References

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Machine-learned potentials construction for high-entropy alloys properties using GPUMD - UVA School of Engineering and Applied Science

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