Create alloy models
This tutorial showcases how to use Alloy Theoretic Automated Toolkit (ATAT) to create random alloys based on the theory of special quasi-random structure (SQS)
Last updated
This tutorial showcases how to use Alloy Theoretic Automated Toolkit (ATAT) to create random alloys based on the theory of special quasi-random structure (SQS)
Last updated
Written by Cheng-Wei Lee (clee2 [at] mines [dot] edu)
Download the source file from the (e.g. atat3_36.tar.gz)
upload the file to HPC
decompress the .tar.gz file
change the directory into the folder
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/
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
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.
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
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
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
bestsqs.out is the SQS that best match the correlation function of the clusters
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
The bestsqs.out can be converted to VASP POSCAR using the
