MultiRef Step by Step Guide Page 1 of 7

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1 MultiRef Step by Step Guide Page 1 of 7 1 Introduction This guide serves as a step by step guide to the installation and set-up of MultiRef as well running refinements on the example dataset provided. Instructions in MATLAB are based on MATLAB R2015a. 2 Obtaining the software The MultiRef Software framework and the example data can be downloaded through the MultiRef website: GSAS with EXPGUID can be downloaded directly from the Advanced Photon Source s website: 3 Installation 3.1 MATLAB If you do not already have a version of MATLAB installed it can be acquired through Mathworks: GSAS Install GSAS using the instructions provided from the download site, preferentially to a location such as C:/GSAS 3.3 MultiRef The MultiRef zip file must be extracted to a convenient location such as C:/MultiRef/mfiles and the folder (including subfolders) must be added to MATLABs search path: Home Set Path Add With Subfolders (Figure 1) Figure 1 4 Example Dataset - MultiRef 4.1 Preparation of files The example dataset must be extracted using e.g. 7-zip. In order to be extracted correctly the files must be numbered XYZ.zip.001, XYZ.zip.002, XYZ.zip.003 and XYZ.zip.004. The files for the example dataset should be placed in a single folder together such as C:/MultiRef/ExampleData with the three executables from the GSAS exe-folder (e.g. C:/GSAS/exe) genles.exe, hstdmp.exe and powpref.exe.

2 MultiRef Step by Step Guide Page 2 of 7 A list of the files that should be located in the folder is seen in Figure 2. Figure 2 The files are: genles.exe: GSAS program, which performs the least square calculations. hstdmp.exe: GSAS program, which is used for plotting the results of an EXP file using plotmodel.m. powpref.exe: GSAS program, which is used for preparing the EXP for refinement. IM_RECON_PK.EXP: GSAS experiment file containing the starting model used by MultiRef for refinement. This file has to be made manually in GSAS by refining representative datasets. im_recon_pk.mat: MATLAB data file containing the data used for the example dataset. two_theta.mat: MATLAB data file containing the 2theta values which match the intensity data in im_recon_pk.mat. x_back.mat: MATLAB data file containing a list of 2theta values where there are no peaks in the data. These are used when calculating a background and can be selected using select_background.m makechoice.m: MATLAB script which is called by MultiRef.m when a choice of refinement route has to be made. This has to be written specifically for the experiment investigated. MultiPlot.m: MATLAB script used to plot the results. MultiRef.m: MATLAB script used to perform the Rietveld refinements. MultiSave.m: MATLAB script used to save the results from MultiRef.m. hstinput.txt: Text file used as input to hstdmp.exe when plotting results. killpowpref: Batch script monitoring powpref.exe. In rare cases, usually when the model is a very poor fit to the data or when powpref.exe is running on a non-converged dataset, powpref.exe will enter an infinite loop (running for more than 1 minute). When this happens killpowpref will close this instance of powpref.exe and MultiRef.m will continue.

3 MultiRef Step by Step Guide Page 3 of Setting up and Running MultiRef.m User Input In this section the basic inputs to the refinement are given. The following lines should be edited and/or checked: Line 6, folder: The location of the files mentioned in section 4.1. Line 8, insfile: The name of the instrument file. Line 10, expfilename: The name of the experiment file without the.exp ending. Line 13, nrowstotal: The total number of rows. Line 15, rows: The row numbers, which will identify the correct row in a matrix or a file number. Line 17, ncolstotal: The total number of columns. Line 19, cols: The column numbers, which will identify the correct column in a matrix or a file number. Line 24, offset: The offset is used when performing partial runs (all columns, some rows). On large datasets it can be an advantage not to run all refinements in one step, but split it into different runs. In order to write the results to the correct positions in the output matrices an offset is used. For example, if your dataset consist of the rows , then put in rows(1)-1253 and when you run on only the lines the results will be written to the correct positions in the matrices. Line 27, npar: The number of parallel threads to be used when running in parallel. The maximum number is the number of CPUs on your computer. Line 30, ncyc: The number of refinement cycles to be performed. Line 33, choicemdim: The dimension of the choice matrix i.e. the number of choices made for a dataset. Two additional inputs are given for the example dataset considered: Line 37-38: The 2theta values are not included in the loaded data files and constant for all data points. These are loaded and converted here. Line 42: A list of background points where no reflections are present are loaded. These have been selected using select_background.m. These are used when calculating the background. See Figure 3 for the points used to calculate the background for diffractograms from the example dataset.

4 MultiRef Step by Step Guide Page 4 of 7 Figure Control Handles As seen in Figure 4 these are handles to control what should be performed by MultiRef. Each handle is set by inputting 1/0 as a true/false statement on whether to perform each step. Start out by setting all handles to 0 except for showgsasinfo and createdatafile. Figure Initialization In this section variables are initialized, files are copied, the parallel pool is started and information from the EXP file is loaded. In principle no user input required here. The user is encouraged to go through the code line by line and understand what happens.

5 MultiRef Step by Step Guide Page 5 of Run Loops In this section the code loops through the two refinement loops, an outer one for the rows and an inner one for the columns. For each data point the correct data should be given in a format readable by GSAS and the refinement should be performed with the correct parameters enabled User Input Depending on the type of data supplied the data file for GSAS can be written by MultiRef to the savefolder (createdatafile = 1), copied to the savefolder (copydatafile = 1) or already located in the folder (createdatafile = 0, copydatafile = 0). In the case of the example dataset the data file has to be created for each data point. In order to create the data file the 2theta, intensity and error estimates has to be supplied as vectors. In MultiRef this is done by first creating a string with the filename of the data file in line 152, loading the data in line 155, selecting the intensity data for the current data point in line 158, checking for NaN s in line 159 and estimating the errors from intensity in line 162. Once these calculations have been performed then in line 165 the intensity data can be written to the data file using the data2fxye.m script. The 2theta data was loaded in the beginning of the script. After the user input a Chebyshev background is fitted to a selected subset of data points if the user has selected this option. If calculating a Chebyshev background with MultiRef, MATLAB might show a warning similar to this: Warning: Polynomial is badly conditioned. Remove repeated data points or try centering and scaling as described in HELP POLYFIT. This is not important for the fitting of the Chebyshev polynomium and can be suppressed by commenting out line 75 in MATLABs polyfit function (which Chebfun s polyfit uses). The function can be found at a location similar to C:\Program Files\MATLAB\R2015a\toolbox\matlab\polyfun\polyfit.m. Depending on your MATLAB configuration it might be necessary to allow the current Windows user to modify the file through the right click menu. Right Click Properties Security Select User Tick Full Control Run Refinements In this section the Rietveld refinements are performed. This is done by reading the input GSAS experiment (EXP) file line by line, editing it if necessary and then writing it to a new EXP file. In line 206 and 207 the input and output EXP files are defined. rungsas.m is called in line 209. This function edits the EXP file and then runs the GSAS executables. If the refinement finishes successfully then rungsas.m outputs 1 and otherwise 0. This can be used to identify data points, where the MultiRef refinement did not converge. When calling rungsas.m some fixed inputs are followed by a variable number of inputs. The variable inputs are used to select how to edit the EXP file. The possible inputs can be found in the rungsas.m script and in Figure 5. This is not an exhaustive list additional possibilities can easily be created using the existing as templates.

6 MultiRef Step by Step Guide Page 6 of 7 Figure 5 In the example dataset two cycles of refinements are carried out and between these a choice is made. In the first refinement cycle two variable inputs are given. The first inserts the name of the data file in the EXP file and the second inserts the previously calculated Chebyshev background coefficients. The refinement of the scale factor and the background is enabled in the input EXP file and these are then refined. After the first refinement cycle, the results are inspected in the makechoice.m function, where the scale factor is compared to the estimated standard deviation of the scale factor to determine whether material is present at the current data point. If this is the case then the second refinement cycle is performed. In the second cycle the refinement of unit cell parameters and profile parameters is enabled Clean Up In this section temporary files are deleted and results are saved. In principle no user input required here. The user is encouraged to go through the code line by line and understand what happens. 5 Example Data Set MultiSave The function MultiSave is used to read the results from the refinements and store them in a file easily readable by MATLAB. In the beginning of the script basic inputs are given about the data to load. The following lines should be edited and/or checked. In this section the basic inputs to the refinement are given. The following lines should be edited: Line 5, rows: The row numbers, which will identify the correct row in a matrix or a file number. Line 7, cols: The column numbers, which will identify the correct column in a matrix or a file number. Line 9, PVEfile: The name of the PVE file without the.pve-ending Line 13, Nvars: the maximum number of variables in the PVE files. This is used to initialize the matrix where the results are saved. Line 16, mainfolder: The folder where the folders from the refinements are found. Line 19, ncyc: The number of refinement cycles performed.

7 MultiRef Step by Step Guide Page 7 of 7 6 Example Data Set MultiPlot MultiPlot serves as an example of how 2D datasets refined with MultiRef can be visualized using MATLAB. It is divided into the following sections: Initialization: In this section the output files from MultiSave are loaded Define mask: Here a mask used when plotting the data is made. Depending on the data investigated the mask can be created using different criteria such as, geometry, goodness-of-fit parameters or refined parameters. ChoiceM: Plot of the choice(s) made. For the example data this is a binary plot. Rwp & Rp: Plot the goodness-of-fit parameters to check the quality and consistency of the fits. Parameter plotting: Select refined parameters and plot them. The parameters are identified by their name in the PVE-files and plotted. The header of the plot should also be updated together with the interval for the colorbar. Integrated Background: The integrated background intensity is calculated from the Chebyshev coefficients. The number of coefficients and the range of the data used for refinement can be loaded from the EXP file or input manually. Additionally, the 2theta values must be supplied.