Computational XRD

Computational XRD

Making Nanoparticles

Here we give an example of how to generate nanoparticles with different dislocations. A python version of the MD++ code is necessary. The relevant python files are:

generate_particles.py XRDsetup.py

and these can be run with the command: bin/eam generate_particles.py

Flags

# flag = 0 is no dislocations
# flag = 1 is only one dislocation
# flag = 2 is multiple randomly generated dislocations
flag = 0 
start = 40    # Radius of the nanoparticle
numDisls = 6  # if flag == 2, the number of dislocations

This generates a series of .cn, .cfg, and a file of atomic positions which will be read by the C++ code to calculate the Intensity.

Intensity Calculation in C++

First, make sure the required libraries are installed. The GSL, C++11, and OpenMP packages are required (the GSL library is for easily making histograms). The required files are the previous file of atomic positions and the attached C++ file:

Now we are ready to run g++ -o XRD.exe XRD_OpenMP.cpp -lgsl -lgslcblas -lm -fopenmp -std=c++11

Plotting and Interpolating

The next step is to read the file of Intensities and Q values into the python script attached. The Intensity and Q values that are either background before the first real Bragg peak or are small values past Q~20 are gotten rid of. The interpolation is only done on the largest 5-10 peaks. The number of peaks that can be reasonably interpolated decreases with increasing number of dislocations as the peaks broaden. There are some important parameters to consider, especially in the interpolation. These are:

Parameters

wavelength = 0.67
Radius = int(sys.argv[1])
flag = int(sys.argv[2])
cutoff = 2e4  # Bragg peaks with maxima below cutoff will be ignored
tol = 0.1     # The Q range around which the program searches for a half-maximum

CUDA Code

The code is obviously parallel. The next step is to re-write the code in CUDA to improve speed.