Create alloy models

Written by Cheng-Wei Lee (clee2 [at] mines [dot] edu)

Installing Alloy Theoretic Automated Toolkit

1. Download the source file from the (e.g. atat3_36.tar.gz)

2. upload the file to HPC

3. decompress the .tar.gz file

1. change the directory into the folder

1. check the makefile within the folder and change the path to binary file (BINDIR) if need

Then it's time to compile the source code. With the provided makefile, we only need to

and if there are no error messages

Sanity check of compiling

If it returns 0, it indicates no error message. The binary like mcsqs can be found in ~/bin/

Preparing input for mcsqs

Here, we are using the Monte Carlo (mcsqs) approach to find the special quasi-random structures (SQS) that match the correlation of random distribution

To start with, we need a "lat.in" file which defines the parent structure. The one for wurtzite AlN is provided below as example

The first six lines define the lattice vectors. The first three lines form a 3x3 matrix M1, which consists of the lattice parameters of the unit cell you want to run SQS with. The following 3 lines form another 3x3 matrix M2. M2 is usually the identity matrix. The final lattice vectors in matrix form will be M1xM2. Here's another example of rocksalt AlN with different M2.

The remaining lines define the basis and the elements. "Al=0.08333333,Gd=0.91666667" is where we can define the Gd content for AlGdN alloys

Another required input is the files that describe the correlation between atoms. It can be generated using corrdump, which requires lat.in

corrdump will create two files, sym.out and clusters.out, which will be needed for the monte carlo simulation.

With lat.in, sym.out, and clusters.out, we can run the mcsqs next

Running Monte Carlo simulation to find SQS

The typical prompt for mcsqs is shown below

To fully utilize all the cpus of a given node. Here's an example SLURM script for Kestrel, which has 104 cores/node

The idea is to initial parallel MC simulations with different initial seeds.

Generate SQS with a specific supercell

mcsqs can generate supercell with very slanted shapes and one way to address this is to specify the supercell via a sqscell.out file but with the caveat that it could require larger supercell to reach desirable mismatches.

Here's the modified flag to run mcsqs:

and here's an exemplar sqscell.out file for the 3x3x2 supercell

Analyze the MC simulation results

The goal of the MC simulation is to find a structure that has a distribution of atoms that perfectly match the correlation function of selected 2- and 3-body clusters. During the mcsqs simulation, there are three outputs: mcsqs.log, bestsqs.out, and bestcorr.out

1. mcsqs.log records the evolution of the MC simulation but only the ones with lower objective functions will be added to it. The mismatches are shown in this file

2. bestsqs.out is the SQS that best match the correlation function of the clusters

3. bestcorr.out save the correlation function values for the best SQS

The goal is to find a SQS with not only low objective function but also small mismatches. It is more important to have lower mismatches if two SQS have similar objective function

Examples of mismatches in clusters for 48-atom, 72-atom, and 96-atom SQS created for wz-Al1Gd11N12 alloys

• 48-atom has some clusters with large mismatch, i.e. number > 0.04

• 72-atom one is acceptable

• 96-atom SQS is the best among the three