Week 1, 6/16-6/20 Erika Roedl
	
	Primary data analysis included graphing the S(q) data and g(r) data for Ni24Zr76 over a range of temperatures and looking at peak height variations in temperature of S(q)1. The higher peaks correspond with a lower temperature, with the glass temperatures considerably higher than the liquid temperatures.
Glass temperatures (corrected) in ºC: 25, 220, 274
Liquid temperatures (corrected)  in ºC: 872, 943, 1033.5, 1123, 1345, 1476

Figure 1: S(q) of all of the above temperatures

Figure 2: Zoomed in on first peak of Figure 1

Figure 3: Temperature (corrected) vs height of the first peak.
The blue line is a fit for the glass data, y = 3.84 - 0.000284x, R^2 = 0.9882
The orange is a fit to the liquid data, y = 3.60 – 0.000744x, R^2 = 0.9971

The position of the first peak did not change as much as expected with temperature, the range was 0.0416.
The g(r) plots were very similar. However for the glassy temperatures, a second, smaller peak appeared, and several oscillations before the largest peak, shown in Figure 4.  This is most likely due to the Nickel content, which is very sensitive to temperature.

Figure 4: g(r)s for the temperatures listed.








	After the data analysis, a reverse-Monte Carlo (RMC) program was run to generate likely positions for the atoms based on the S(q) data, a .dat file, and a .cfg file.
The S(q) was converted from a csv to a .sq file using the python program ColumnReducerForSq.py, which only contained q, S(q), Ni-Ni, 2*Ni-Zr, and Zr-Zr. The Ni-Zr given in the original file needed to be multiplied by 2. 
The .dat file contains the density, the maximum distance to move any particular atom for each step, how long to run the MC, and the name of the .sq file to read from, among other information.
The .cfg file is created by Random.exe, which generates random initial positions for the atoms. The RMC edits this file at intervals dictated in the .dat file. To avoid an error, an extra line must be entered 2 lines down into the cfg file.
The .cfg, .dat, and .sq files must all be named the same to avoid error.
For the data taken at 1200 ºC, the χ^2 = 2.9364
Next, another python program, SpreadsheetFileDelimiterConverter2.py, was run that takes in the .fit file that RMC produced, and converts it into a .csv file. This was then the input for PlotFitData.m in Matlab to visually compare the fit. 


Figure 5: S(q) of 
RMC and experimental.
The generated peak height is less than 0.05 smaller than the experimental. This good quality fit is expected for liquid temperatures.