# Week 4

This week was split into many parts. These parts were finishing Voronoi analysis from the previous week, density workups and the exploration of LabView, varying number density and see how this affects the Reverse Monte Carlo Simulation and its outputs, and lastly beginning Constrained RMC’s. The Voronoi index counts were obtained and the top 20 Voronoi indices were obtained before and after outlier removal.There was a difficulty in finding the outliers at 233C in order to solve this problem the RMC was ran again on a newly generated configuration file. This ran for 24 hours like the rest of the glasses. Afterwards the outliers were able to be found and the top 20 Voronoi indices before and after outlier removal. In addition to this the counts for certain indices were obtained. This week has also been dedicated to measuring the densities of various composition glasses. This was done using Archimedes’ Method. It should be noted that the mass measurements found in the air are easy to obtain and take less than an hour for 10 measurements. However the toluene measurements are much harder to obtain. The process has taken as long as 3 hours obtaining stable measurements. I have found slight ways to decrease this time and increase the stability and precision of these measurements. First the toluene should be removed from the storage, placed in a container, holding the appropriate volume, outside and covered as to approach the room’s temperature before taking measurements. Another method is to look at the minute oscillations that hold fairly stable and find the value that is being fluctuated around. The next method is to use Lab View to automate these measurements, this project has led me to watch many tutorial videos on the National Instruments’ site, doing the programming tutorials found on their site, completing their quizzes, and reading the user manual for the Cahn C-35 micro-balance in order to find how to interface with the balance. To further investigate the Reverse Monte Carlo simulation and the accuracy of the number densities that were given, and used in the earlier runs. This was done by looking at the highest temperature glass and decreasing the number density by 5% and extrapolating the liquid data to get another value that turned out to be 18.5% higher than the original value. These runs were then compared by looking at the fit to the S(q) and the partial g(r)’s. This varying of the number density was also tested on the highest temperature liquid changing the number density by decreasing it by 5%,10%, and 15% of the original value. The comparison was done the same way. The results illustrate for the glass that the given density is likely to be within a 5% of the true value, and probably even more precise than that. The liquid data is harder to interpret however there is a clear difference between the number densities in the fits and that leads me to believe that the value given is more precise than the five percent difference illustrated in my test. Lastly this week I have begun the constrained RMC by including Molecular Dynamic data in the RMC simulation. This was run initially for 12 hours on the highest temperature liquid. The fit was very choppy and could use more time so it was ran for another 12 hour increment. The change in procedure is minimal but more set up is necessary so this process has begun. The main differences when compared to the unconstrained RMC are the additional .gr files and the .dat file that has been altered to accommodate the additional information. Finishing Voronoi from Unconstrained of Zr36Ni64: Voronoi_2_1_233 Voronoi_2_2_233 Voronoi_2_408. Density Spreadsheets were not allowed for “security reasons” in current format. Varying the number density results: Insert_05383 Insert_05666 Insert_06258 Insert_053193 Insert_056322 Insert_059451 Insert_067128 P_VND_1444

Lastly the Constrained RMC example files were also not allowed to be attached however they w