huy310304 / gpumd-uva

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

GPUMD-UVA (In-progress)

📝 A brief summarization poster

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.

Recent Work and Updates (Summer 2024)

• Running MD simulation with CoNiO and MgNiCuCoZnO using the J14 potential.
• Tuning the atom's concentration in the alloys to see how much can the Thermal Conductivity be tuned.

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 different HEAs.
• 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

• MgNiO Potential

• J14 Potential

• CoNiO Potential

References

Documents and Tutorials

• GPUMD Document Page

• NEMD Running Tutorial

• HNEMD Running Tutorial

• GPUMD Source Code

• UVA Rivanna

Related Papers

• Charge-Induced Disorder Controls the Thermal Conductivity of Entropy-Stabilized Oxides