MonteCarlo3D

Description

3-dimensional Monte Carlo simulation of high entropy alloys

• Cannonical ensemble (fixed temperature).
• Effective pair interaction (EPI) model.
• Temperature parallelization.

For more information about EPI and high entropy alloys, you may check the following papers:

• Robust data-driven approach for predicting the configurational energy of high entropy alloys
• Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach

Software Architecture

Software architecture description

Installation

Make sure you have the following packages already installed:

• gcc: support -std=c++11.
• MPI: support mpicxx, OpenMPI is used for testing, but the program should also works for MPICH.
• python: >=3.0, include numpy, scipy, and matplotlib.
• If you want to use the notebook, please install Jupyter Notebook.

Instructions

Example:

1. Generate "neighbors.dat" in "./Python/neighbor_tmp" via

   python ./Python/genNeighbors.py

   • Note that the first line in "neighbors.dat" is the distances of neighboring atoms to the centering atom. The radiu-cutoff r_max can be set in the main function of "genNeighbors.py".
   • The supercell size can also be set in the main function of "genNeighbors.py" by setting "n_repeats=[4,4,4]" to other values (by default, ).
   • The "EPI.dat" are the energy model parameters for the pair-interaction model. They can be obtained from the linear regression of energy vs EPI. By default, it includes at most 6 neighboring shells.

2. Run Monte Carlo simulation via

   cd ./C++
   mpicxx -O3 -std=c++11 MonteCarlo.cpp
   mpirun -n 4 ./a.out

   • The supercell size is obtained from the "neighbors.dat" mentioned above.
   • Temperature parallelization is implemented with MPI. The following variables can be set in the main funciton:
     • T_min and T_max: minimum and maximum temperatures.
     • n_T: the number of different temperatures. parallelized with MPI.
     • n_measure: the number of sweeps used for making measurement.
     • n_discard: the number of sweeeps discarded between each measurement.
     • You can save the output to a file with

       mpirun -n 4 ./a.out > ../Output/output.dat

       Note that the outputs start with a bunch of TEST, and the only the last TEST carries out the Monte Carlo simulation

3. You can also ignore the above steps, and simply run

   ./run.sh

Contribution

1. Fork the repository
2. Create Feat_xxx branch
3. Commit your code
4. Create Pull Request

Contact Info

xianglinliu01@gmail.com