Discrepancy between our XRD and vesta

For the same input

(base) qiangzhu@Qiangs-MacBook-Pro-2 XRD (master) $ python scripts/pxrd.py -c dataset/Acmm-POSCAR
  2theta     d_hkl     hkl       Intensity  Multi
  14.124     6.271   [ 0  0  2]    10.96        2
  15.486     5.722   [ 1  0  0]     4.73        2
  21.018     4.227   [ 1  0  2]     0.08        4
  23.089     3.852   [ 1  1  1]    50.54        8
  28.469     3.135   [ 0  0  4]    13.68        2
  30.747     2.908   [ 1  1  3]   100.00        8
  31.237     2.863   [ 0  2  0]    23.83        2
  31.264     2.861   [ 2  0  0]    20.41        2
  32.566     2.750   [ 1  0  4]     0.98        4

While the vesta output is

The intensity for [002] is very different.

@sayred1 please check what happened.

Thanks for the extra insight, I will begin looking into this.

On Mon, Feb 10, 2020 at 9:24 PM Qiang Zhu notifications@github.com wrote:

For the same input

(base) qiangzhu@Qiangs-MacBook-Pro-2 XRD (master) $ python scripts/pxrd.py -c dataset/Acmm-POSCAR 2theta d_hkl hkl Intensity Multi 14.124 6.271 [ 0 0 2] 10.96 2 15.486 5.722 [ 1 0 0] 4.73 2 21.018 4.227 [ 1 0 2] 0.08 4 23.089 3.852 [ 1 1 1] 50.54 8 28.469 3.135 [ 0 0 4] 13.68 2 30.747 2.908 [ 1 1 3] 100.00 8 31.237 2.863 [ 0 2 0] 23.83 2 31.264 2.861 [ 2 0 0] 20.41 2 32.566 2.750 [ 1 0 4] 0.98 4

While the vesta output is [image: image] https://user-images.githubusercontent.com/29445366/74212894-6e98fe80-4c4b-11ea-9f3f-83ea96e04698.png

The intensity for [002] is very different.

@sayred1 https://github.com/sayred1 please check what happened.

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I found a discrepancy between pymatgen and our code. Here's a comparison of two outputs for NaCl:

compare intensities (pymatgen, us)
8.113939804879704 8.113939804879704
100.0 100.0
65.90374391470813 65.90374391470813
1.9590067734136543 1.9590067734136538
21.35834030306311 21.358340303063105
9.382643886507328 3.1275479621691096 [different by a factor of 3]
0.896811464666153 0.896811464666153 [okay]
24.881082377145024 8.293694125715007 [different by a factor of 3]
18.256136417593925 6.085378805864641 [different by a factor of 3]
1.061205191351555 0.26530129783788864 [different by a factor of 3]

If we also compare the output of hkl planes and multiplicity, we can find why there is a difference. Note that for hkl planes (4,0,0), (4, 2, 0), (4, 2, 2), pymatgen has multiplicities 3 times the amount of ours. Furthermore, we are missing the (5, 1, 1) plane.

compare hkl planes (pymatgen, us)
[{'hkl': (1, 1, 1), 'multiplicity': 8}] [{'hkl': (1, 1, 1), 'multiplicity': 8}]
[{'hkl': (2, 0, 0), 'multiplicity': 6}] [{'hkl': (2, 0, 0), 'multiplicity': 6}]
[{'hkl': (2, 2, 0), 'multiplicity': 12}] [{'hkl': (2, 2, 0), 'multiplicity': 12}]
[{'hkl': (3, 1, 1), 'multiplicity': 24}] [{'hkl': (3, 1, 1), 'multiplicity': 24}]
[{'hkl': (2, 2, 2), 'multiplicity': 8}] [{'hkl': (2, 2, 2), 'multiplicity': 8}]
[{'hkl': (4, 0, 0), 'multiplicity': 6}] [{'hkl': (0, 4, 0), 'multiplicity': 2}]
[{'hkl': (3, 3, 1), 'multiplicity': 24}] [{'hkl': (3, 3, 1), 'multiplicity': 24}]
[{'hkl': (4, 2, 0), 'multiplicity': 24}] [{'hkl': (2, 4, 0), 'multiplicity': 8}]
[{'hkl': (4, 2, 2), 'multiplicity': 24}] [{'hkl': (2, 4, 2), 'multiplicity': 8}]
[{'hkl': (3, 3, 3), 'multiplicity': 8}, {'hkl': (5, 1, 1), 'multiplicity': 24}] [{'hkl': (3, 3, 3), 'multiplicity': 8}]

This lead me to believe that our missing multiplicities are due to us missing hkl planes to evaluate, which is true. Pymatgen evaluates 619 planes, while we consider 416.

Ours ranges from

[[-3 -3 -3]
 [-3 -2 -3]
 [-3 -1 -3]
 ...
 [ 3  1  3]
 [ 3  2  3]
 [ 3  3  3]]

So we're missing the [-4 -4 -4] to [4 4 4] and [-5 -5 -5] to [5 5 5] planes. So I think that the missing multiplicities are from the (4,2,2), (4,2,0), etc. My thoughts are that the issues are from the following lines https://github.com/qzhu2017/XRD/blob/ee6d98de012b93aeebb46cf8844ed4945ef02163/pyxtal_xrd/XRD.py#L91-L92.

Before I can tackle this issue, I have a coding project due for class tomorrow night. Once I'm done, I can fix this problem, check with VESTA, and continue bayesian optimization hopefully by tomorrow.

@sayred1 can you talk for a few minutes?

@sayred1 I have fixed this issue. I don't know why you create a 3D array for hkl_index [26,1,3]. Now I did it with [26, 3]. The results are consistent now.

        hkl_index = np.array([[[-1,-1,-1]],[[-1,-1,0]],[[-1,-1,1]],[[-1,0,-1]],[[-1,0,0]],[[-1,0,1]],[[-1,1,-1]],[[-1,1,0]],[[-1,1,1]],
                     [[0,-1,-1]],[[0,-1,0]],[[0,-1,1]],[[0,0,-1]],[[0,0,1]],[[0,1,-1]],[[0,1,0]],[[0,1,1]],
                     [[1,-1,-1]],[[1,-1,0]],[[1,-1,1]],[[1,0,-1]],[[1,0,0]],[[1,0,1]],[[1,1,-1]],[[1,1,0]],[[1,1,1]]])

        for index in hkl_index:
            d = float(np.linalg.norm( np.dot(index, crystal.rec_matrix),axis = 1))

[[{'hkl': (1, 1, 1), 'multiplicity': 8}], [{'hkl': (2, 0, 0), 'multiplicity': 6}], [{'hkl': (2, 2, 0), 'multiplicity': 12}], [{'hkl': (3, 1, 1), 'multiplicity': 24}], [{'hkl': (2, 2, 2), 'multiplicity': 8}], [{'hkl': (4, 0, 0), 'multiplicity': 6}], [{'hkl': (3, 3, 1), 'multiplicity': 24}], [{'hkl': (4, 2, 0), 'multiplicity': 24}], [{'hkl': (4, 2, 2), 'multiplicity': 24}], [{'hkl': (5, 1, 1), 'multiplicity': 24}, {'hkl': (3, 3, 3), 'multiplicity': 8}]]

@qzhu2017 There are several differences between our XRD calculations and VESTA, seen in the calculation of the structure factor.

1) Atomic scattering parameters 2) Scattering factor formula 3) Lorentz factor formula

The parameters and formulas above can be easily changed to match theirs, however changing these isn't sufficient enough to obtain the same structure factor as VESTA. I think we must also include the following, which they include:

5) Occupancies in structure factor 6) Debye-Waller factor

There's a bit of information here to show you, I'll stop by your office after I grab lunch.

So before using VESTA's functionality (with our original functions and atomic scattering parameters), here are the results (for NaCl):

compared to VESTA:

Now if I code in VESTA's functionality + the atomic scattering parameters + debye-waller factors (discussed https://tkawaguchi.com/debye-waller-factor/), our code gives:

It seems that the debye factor is working since the intensity and structure factor match better for higher angles. According to VESTA's manual (https://tkawaguchi.com/debye-waller-factor/ pg 111), the debye, lorentz, and occupancy factors are the only correction factors included, but I still seem to be missing something.

I figured out why we were getting weird results yesterday with changing 2theta values, this was due to the file we were using. The NaCl.cif and NaCl-POSCAR files in my repo had different unit cell parameters, and after testing the cif file, I get the original 2theta values we saw in the previous tables. Now, I am able to get very close to vesta's results.. So, for the NaCl.cif file with:

_cell_length_a                   5.64167
_cell_length_b                   5.64167
_cell_length_c                   5.64167

It's hard to tell if I'm still missing something with their structure factor calculation, since our formalism is exactly the same:

coeffs = [ [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01,   1.23565E-01],[-2.88730E-03, 0]],
        [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01,   1.23565E-01],[-2.88730E-03, 0]],
        [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01,   1.23565E-01],[-2.88730E-03, 0]],
        [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01,   1.23565E-01],[-2.88730E-03, 0]],
        [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801  ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01,   7.00107E-01], [-4.47180E-03, 0]],
        [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801  ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01,   7.00107E-01], [-4.47180E-03, 0]],
        [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801  ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01,   7.00107E-01], [-4.47180E-03, 0]],
        [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801  ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01,   7.00107E-01], [-4.47180E-03, 0]]
         ]

f0 = np.sum(coeffs[:, :5, 0] * np.exp(-coeffs[:, :5, 1] * s2), axis = 1) +  coeffs[:, 5, 0]
f1 = coeffs[:, 6, 0]
f2 = coeffs[:, 6, 1] 
f_NT = coeffs[:, 7, 0]
sf =  f0 + f1 + 1j*f2 +f_NT

How about the other structure?

On Fri, Feb 14, 2020 at 6:14 PM Dean Lewis Sayre Jr. < notifications@github.com> wrote:

I figured out why we were getting weird results yesterday with changing 2theta values, this was due to the file we were using. The NaCl.cif and NaCl-POSCAR files in my repo had different unit cell parameters, and after testing the cif file, I get the original 2theta values we saw in the previous tables. Now, I am able to get very close to vesta's results.. So, for the NaCl.cif file with:

_cell_length_a 5.64167 _cell_length_b 5.64167 _cell_length_c 5.64167

[image: Screen Shot 2020-02-14 at 5 57 00 PM] https://user-images.githubusercontent.com/35546511/74579908-6db9e280-4f53-11ea-9004-089530eb9271.png

It's hard to tell if I'm still missing something with their structure factor calculation, since our formalism is exactly the same:

coeffs = [ [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01, 1.23565E-01],[-2.88730E-03, 0]], [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01, 1.23565E-01],[-2.88730E-03, 0]], [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01, 1.23565E-01],[-2.88730E-03, 0]], [[4.910127, 3.281434],[3.081783, 9.119178],[1.262067, 0.102763],[1.098938,132.013942] ,[0.560991, 0.405878], [0.079712,0.0], [1.33491E-01, 1.23565E-01],[-2.88730E-03, 0]], [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801 ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01, 7.00107E-01], [-4.47180E-03, 0]], [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801 ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01, 7.00107E-01], [-4.47180E-03, 0]], [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801 ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01, 7.00107E-01], [-4.47180E-03, 0]], [[1.446071, 0.052357],[6.870609, 1.193165],[6.151801 ,18.343416],[1.750347,46.398394],[0.634168 ,0.401005],[0.146773,0.0], [3.56753E-01, 7.00107E-01], [-4.47180E-03, 0]] ]

f0 = np.sum(coeffs[:, :5, 0] np.exp(-coeffs[:, :5, 1] s2), axis = 1) + coeffs[:, 5, 0] f1 = coeffs[:, 6, 0] f2 = coeffs[:, 6, 1] f_NT = coeffs[:, 7, 0] sf = f0 + f1 + 1j*f2 +f_NT

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--

*Qiang Zhu, *Assistant Professor 4505 S. Maryland Parkway, Room 232, Department of Physics and Astronomy, University of Nevada Las Vegas NV 89154-4002

Phone: 702-895-1707 Fax: 702-895-0804

Webpage: http://www.physics.unlv.edu/~qzhu http://uspex.stonybrook.edu/qzhu.html

For the Acmm.cif structure, VESTA doesn't specify a debye-waller parameter (U) for Pb, which denotes the mean-squared displacement of the atom. So, I just set this to 1.

I've attached the vesta file for comparison. I think that an assumption of 1 may be the reason why some of the values of |F| are off, but there is still close agreement. Acmm.txt

(hkl)      2-theta        d        I      |F|     m
(0, 0, 2)   14.113   6.2705   100000  81.4859     2
(1, 0, 0)   15.473   5.7221    38799  55.5787     2
(1, 0, 2)   21.001  4.22674      798   7.6022     4
(1, 1, 1)    23.07  3.85215      364   3.9792     8
(0, 0, 4)   28.445  3.13525    21313  74.3586     2
(1, 1, 3)   30.721  2.90796    66860   70.811     8
(0, 2, 0)   31.211  2.86345     1317  20.1762     2
(2, 0, 0)   31.238  2.86105      807    15.81     2
(1, 0, 4)   32.539  2.74957       28   2.1779     4
(0, 2, 2)   34.403  2.60472    46630  92.9302     4
(2, 0, 2)   34.427  2.60291    44009   90.341     4
(1, 2, 0)   35.013  2.56072      911  13.2041     4
(2, 1, 1)   35.777  2.50774     1933  13.8724     8
(1, 2, 2)   37.923  2.37066      143   3.9847     8
(2, 1, 3)   41.329   2.1828     1696  14.8015     8
(1, 1, 5)   42.359  2.13207    12430  40.9511     8
(0, 2, 4)   42.732  2.11434       25   2.6656     4
(2, 0, 4)   42.752  2.11337       71   4.4289     4
(0, 0, 6)    43.25  2.09017        9   2.3481     2
(2, 2, 0)   44.741  2.02392    20626  78.2618     4
(1, 2, 4)   45.709  1.98328      595   9.5796     8
(1, 0, 6)   46.202  1.96329      296   9.6526     4
(2, 2, 2)   47.148  1.92607       59    3.098     8
(3, 0, 0)   47.639  1.90737      715   21.752     2
(3, 0, 2)   49.937  1.82481      656   15.327     4
(1, 3, 1)   50.908  1.79227     6843  35.5693     8
(2, 1, 5)   50.935   1.7914      800  12.1667     8
(3, 1, 1)   50.944  1.79109     5330  31.4089     8
(2, 2, 4)   53.874   1.7004    21569  66.1357     8
(0, 2, 6)   54.293  1.68824    11171  67.7331     4
(2, 0, 6)   54.311  1.68775    10508  65.7087     4
(1, 3, 3)   55.236  1.66165     5302   33.452     8
(3, 1, 3)    55.27  1.66071     4129  29.5381     8
(1, 1, 7)   56.093  1.63828     5033  32.9926     8
(3, 0, 4)   56.422  1.62951      803  18.7228     4
(1, 2, 6)   56.812  1.61924       51   3.3707     8
(3, 2, 0)   58.057  1.58743      418  13.8273     4
(2, 3, 1)   58.545  1.57537      512   10.884     8
(0, 0, 8)   58.862  1.56762     4219  62.7249     2
(3, 2, 2)   60.073  1.53889     1301  17.6877     8
(1, 0, 8)   61.259  1.51191       22   3.3026     4
(2, 3, 3)   62.518  1.48445      416  10.3121     8
(1, 3, 5)    63.29  1.46819     3441  29.8962     8
(2, 1, 7)   63.313  1.46772      400  10.2022     8
(3, 1, 5)   63.321  1.46755     2681  26.4011     8
(2, 2, 6)   63.982  1.45398       14   1.9307     8
(0, 4, 0)   65.098  1.43172     2974   56.728     2
(4, 0, 0)   65.159  1.43052     2305  49.9771     2
(3, 2, 4)   65.899  1.41625      585  12.6891     8
(3, 0, 6)   66.286  1.40891      287  12.6407     4
(0, 4, 2)    66.99   1.3958       19   3.2852     4
(4, 0, 2)   67.051  1.39469       30    4.131     4
(1, 4, 0)   67.367  1.38891       18   3.2547     4
(4, 1, 1)   67.891  1.37946     1144  18.1123     8
(0, 2, 8)   68.139  1.37505       19   3.3833     4
(2, 0, 8)   68.154  1.37478        7   2.1271     4
(1, 4, 2)   69.228  1.35604      137   6.3515     8
(2, 3, 5)   70.078  1.34167      292   9.3576     8
(3, 3, 1)   70.086  1.34154     1959  24.2139     8
(1, 2, 8)   70.359  1.33699      128   6.2214     8
(1, 1, 9)   71.555  1.31756     2357  26.9188     8
(4, 1, 3)   71.578  1.31718      973  17.3033     8
(0, 4, 4)   72.522  1.30236     4104  50.6615     4
(4, 0, 4)    72.58  1.30145     3165  44.5156     4
(3, 3, 3)   73.725  1.28405     1681  23.1673     8
(2, 4, 0)   73.973  1.28036        9   2.5197     4
(4, 2, 0)   74.016  1.27971       13   2.9609     4
(1, 3, 7)   74.432  1.27359     2094  26.0036     8
(3, 1, 7)   74.461  1.27317     1632  22.9653     8
(1, 4, 4)   74.687  1.26988       32   3.2362     8
(3, 2, 6)   75.082  1.26417      611  14.1244     8
(2, 4, 2)   75.764  1.25447     6645  46.8177     8
(0, 0, 10)   75.791   1.2541       11   3.9717     2
(4, 2, 2)   75.807  1.25387     5453  42.4284     8
(2, 2, 8)   76.857  1.23934     6333  46.0909     8
(1, 0, 10)   77.924  1.22502       43   5.4147     4
(2, 1, 9)   78.014  1.22382      212   8.5131     8
(4, 1, 5)   78.739  1.21437      733  15.9013     8
(3, 0, 8)   78.995  1.21108      257  13.3546     4
(2, 3, 7)   80.813  1.18835      191    8.246     8
(3, 3, 5)   80.821  1.18826     1283   21.338     8
(2, 4, 4)   81.062  1.18533       25   2.9835     8
(4, 2, 4)   81.104  1.18482       12   2.0839     8
(0, 4, 6)   81.406  1.18119       25   4.3018     4
(4, 0, 6)   81.462  1.18051        5   2.0481     4
(1, 4, 6)   83.501   1.1568       65    4.887     8
(0, 2, 10)   84.219  1.14876     2503  43.0237     4
(2, 0, 10)   84.233   1.1486     2344  41.6414     4
(3, 4, 0)   84.556  1.14503      205  12.3532     4
(5, 0, 0)   84.612  1.14442      194  16.9976     2
(4, 3, 1)   85.015  1.14003      589  14.8236     8
(3, 4, 2)   86.291  1.12641      300   10.653     8
(1, 2, 10)   86.303  1.12628       27   3.2346     8
(5, 0, 2)   86.347  1.12582      434  18.1433     4
(1, 5, 1)    87.04  1.11862     1334  22.5531     8
(5, 1, 1)   87.124  1.11777      586  14.9608     8
(3, 2, 8)   87.354  1.11542      292  10.5681     8
(1, 3, 9)   88.457  1.10434     1276  22.2129     8
(4, 3, 3)   88.479  1.10411      527   14.279     8
(3, 1, 9)   88.485  1.10406      995  19.6184     8
(1, 1, 11)   89.165  1.09739     1249  22.0461     8
(4, 1, 7)   89.187  1.09718      516  14.1718     8
(2, 4, 6)   89.744   1.0918     3845  38.7773     8
(4, 2, 6)   89.786   1.0914     3136  35.0246     8
(1, 5, 3)     90.5  1.08464     1200  21.7366     8
(5, 1, 3)   90.584  1.08386      527  14.4195     8
(3, 3, 7)   91.236   1.0778      916  19.0555     8
(3, 4, 4)   91.481  1.07555      318  11.2416     8
(5, 0, 4)   91.537  1.07504      322  16.0107     4
(2, 2, 10)   92.536  1.06603       30   3.4748     8
(5, 2, 0)   92.914  1.06269      348  16.7104     4
(2, 5, 1)   93.273  1.05953      129   7.2087     8
(0, 4, 8)   93.545  1.05717     1814  38.2451     4
(4, 0, 8)   93.601  1.05669     1382  33.3892     4
(3, 0, 10)   94.631  1.04788      123  10.0216     4
(5, 2, 2)   94.647  1.04775      599  15.6015     8
(2, 3, 9)   94.692  1.04737      124   7.1041     8
(0, 0, 12)   94.964  1.04508      868  37.6016     2
(2, 1, 11)   95.402  1.04144      121   7.0527     8
(4, 3, 5)   95.404  1.04142      432  13.2831     8
(1, 4, 8)   95.629  1.03958       25   3.2259     8
(2, 5, 3)   96.746  1.03052      117    6.957     8
(1, 0, 12)   97.053  1.02808       12   3.2231     4
(1, 5, 5)   97.438  1.02503      991  20.2371     8
(5, 1, 5)   97.522  1.02437      436  13.4252     8
(4, 4, 0)   99.139  1.01196     1172   31.255     4
(5, 2, 4)   99.878  1.00645      564  15.3645     8
(3, 4, 6)  100.181  1.00422      222   9.6402     8
(5, 0, 6)  100.238  1.00381      279  15.2995     4
(4, 4, 2)  100.895  0.99903        3   1.1565     8
(2, 4, 8)  101.936  0.99164       31   3.6257     8
(4, 2, 8)  101.978  0.99134        3   1.1488     8
(3, 2, 10)   103.03  0.98406      221   9.6757     8
(4, 1, 9)  103.136  0.98334      357     12.3     8
(0, 2, 12)  103.372  0.98174       26   4.7187     4
(2, 0, 12)  103.386  0.98164       15   3.6325     4
(2, 5, 5)   103.78  0.97899       99   6.4901     8
(3, 5, 1)  103.787  0.97894      665  16.7943     8
(5, 3, 1)  103.845  0.97856      375  12.6147     8
(3, 3, 9)  105.249  0.96932      645  16.5607     8
(1, 2, 12)  105.516   0.9676       25   3.2801     8
(1, 3, 11)  105.955   0.9648      814    18.62     8
(4, 3, 7)  105.978  0.96465      336  11.9696     8
(3, 1, 11)  105.984  0.96462      635  16.4454     8
(4, 4, 4)  106.234  0.96304     1960  28.8856     8
(3, 5, 3)  107.379  0.95592      618  16.2307     8
(5, 3, 3)  107.437  0.95556      348  12.1914     8
(0, 6, 0)  107.613  0.95448       12    4.678     2
(6, 0, 0)  107.745  0.95368       12   4.5809     2
(1, 5, 7)  108.092  0.95158      781  18.2496     8
(5, 1, 7)   108.18  0.95105      343  12.1071     8
(5, 2, 6)  108.812  0.94729      456  13.9591     8
(0, 6, 2)  109.437  0.94361     1205  32.0714     4
(0, 4, 10)  109.479  0.94337       25   4.6523     4
(4, 0, 10)  109.539  0.94303        1   1.1316     4
(6, 0, 2)   109.57  0.94284      629  23.1838     4
(1, 6, 0)  109.806  0.94148       10   3.0431     4
(1, 1, 13)   110.34  0.93841      749  17.8747     8
(6, 1, 1)  110.396  0.93809      565  15.5367     8
(1, 6, 2)  111.655  0.93104       23   3.1724     8
(1, 4, 10)  111.698   0.9308       20   2.9455     8
(2, 2, 12)  112.101  0.92859     2140  30.1931     8
(2, 3, 11)  112.558  0.92612       84   5.9808     8
(3, 4, 8)  112.832  0.92464      173   8.5908     8
(5, 0, 8)  112.892  0.92432      215  13.5475     4
(6, 1, 3)  114.134  0.91778      531  15.0232     8
(3, 0, 12)  114.376  0.91652       83   8.4382     4
(2, 5, 7)  114.789  0.91441       81   5.8635     8
(3, 5, 5)  114.797  0.91436      543   15.173     8
(5, 3, 5)  114.859  0.91405      306   11.397     8
(0, 6, 4)  115.044  0.91311       24   4.5535     4
(6, 0, 4)  115.183  0.91241       19   4.1061     4
(4, 4, 6)  115.497  0.91082        3   1.1311     8
(2, 6, 0)  116.588  0.90543      992  28.9297     4
(6, 2, 0)  116.712  0.90482      553  21.5959     4
(2, 1, 13)   117.15   0.9027       78   5.7446     8
(1, 6, 4)  117.359   0.9017       17   2.6768     8
(2, 6, 2)  118.539  0.89613       29   3.5261     8
(2, 4, 10)  118.584  0.89592     1928   28.408     8
(0, 0, 14)  118.613  0.89579       11   4.4771     2
(4, 2, 10)  118.631   0.8957     1562  25.5642     8
(6, 2, 2)  118.665  0.89554       36   3.9274     8
(5, 4, 0)  119.014  0.89393      201  12.9789     4
(4, 5, 1)  119.474  0.89184      270  10.6089     8

I'm going to check another structure from the web-site for more validation.

@sayred1 The output intensity for 002 is 45.0527 from VESTA, |F|=159.33. Why did you say the agreement is close?

Also, please try to focus on one thing at a time from now on! I don't quite understand why this simple task takes such long time. And it seems that you are not quite clear what you are supposed to do. Please consider this is your own project, but not an assignment from someone else.

@qzhu2017 I apologize, I should've been more specific here. In regards to the close agreement, I meant to link the output file as opposed to the input file:

Allow me to restart on another footing here. Previously as I mentioned, U for Pb was set to 1 since VESTA's output file didn't show a value. The following results were what you saw, disagreement between our output and theirs. However, in the VESTA console where we obtain the values for f', f'', and f_NT, VESTA gives a value for U = 1.349 for Pb, which isn't shown in the output file. Using this, instead of 1, we see better agreement. With some exceptions (like a few of the first ten planes listed in the output file) there is pretty good agreement between our computed and Vesta's computed |F| values. Acmm copy.txt

Then I wanted to check another structure for further validation, but didn't see much agreement. The structure I checked now (space group Abmm), has a value U which is the same for all atomic species, which doesn't make sense to me according to here.

For our simulations, here are the results, compared to Abmm copy.txt :

(0, 0, 2)     15.438    5.735    37742  50.6843     2
(1, 0, 0)     22.319     3.98    46190  80.3926     2
(0, 2, 0)     23.485    3.785   100000 124.2542     2
(1, 1, 1)     26.446  3.36753    40728  44.4369     8
(1, 0, 2)     27.252  3.26976    49739  71.4643     4
(0, 2, 2)     28.227  3.15902    55727  78.2136     4
(0, 0, 4)     31.166   2.8675     1724  21.3623     2
(1, 2, 0)     32.622  2.74275    87733  112.443     4
(1, 1, 3)     34.594  2.59073    35017   53.041     8
(1, 2, 2)     36.277  2.47434     9526  28.8982     8
(1, 0, 4)      38.67  2.32655     3697  26.9836     4
(0, 2, 4)      39.39  2.28564    17583  59.8289     4
(1, 3, 1)      43.14  2.09526    15611  43.2175     8
(2, 0, 0)     45.546     1.99      496   16.155     2
(1, 2, 4)     45.739  1.98205      241   5.6573     8
(1, 1, 5)     47.245  1.92234    33773  68.7695     8
(0, 0, 6)     47.525  1.91167      106   7.7671     2
(2, 1, 1)     47.886  1.89808    10290  38.3976     8
(0, 4, 0)     48.036   1.8925    26795 124.2501     2
(2, 0, 2)     48.375  1.88003      428  11.1825     4
(1, 3, 3)     48.889  1.86147    11681  41.6331     8
(0, 4, 2)     50.759  1.79718     2764  29.5569     4
(2, 2, 0)     51.866  1.76139      255   9.1523     4
(1, 0, 6)     53.105   1.7232     2090  26.6684     4
(2, 1, 3)     53.242  1.71909    16885  53.7115     8
(1, 4, 0)     53.577  1.70912    11252  62.3233     4
(0, 2, 6)      53.67  1.70638      670  15.2292     4
(2, 2, 2)      54.45  1.68377     7778  37.1207     8
(1, 4, 2)     56.106  1.63793    12868  48.8951     8
(2, 0, 4)      56.22  1.63488    10237  61.7744     4
(0, 4, 4)     58.376  1.57951     1774  26.4805     4
(1, 2, 6)     58.834  1.56831       58   3.4349     8
(1, 3, 5)     59.123  1.56134    16993  58.5187     8
(2, 3, 1)     59.673  1.54825     5193  32.5802     8
(2, 2, 4)      61.76  1.50086     6925  38.6041     8
(1, 1, 7)     62.459  1.48572      275   7.7608     8
(2, 1, 5)     62.992  1.47443    23207  71.7012     8
(1, 4, 4)     63.294  1.46812     1421  17.8051     8
(2, 3, 3)     64.356  1.44642    10845  49.7806     8
(0, 0, 8)     64.995  1.43375    10126  96.8842     2
(1, 5, 1)     66.525  1.40443     4547  32.9984     8
(2, 0, 6)     67.938  1.37861     1119  23.4925     4
(2, 4, 0)     68.347  1.37137      329  12.7987     4
(1, 0, 8)     69.648  1.34889     4653  48.6994     4
(0, 4, 6)     69.883  1.34492      110   7.5077     4
(0, 2, 8)     70.131  1.34078    10257   72.631     4
(2, 4, 2)     70.554  1.33377      654  13.0259     8
(1, 5, 3)     70.963  1.32709     4731  35.1516     8
(3, 0, 0)     70.989  1.32667     2303  49.0595     2
(1, 3, 7)      72.74  1.29899      281   8.7173     8
(3, 1, 1)     72.781  1.29835      205   7.4494     8
(2, 2, 6)     72.976  1.29536     6248  41.1192     8
(3, 0, 2)     73.162  1.29253      778  20.5549     4
(2, 3, 5)     73.235  1.29143    16343  66.6508     8
(1, 4, 6)     74.394  1.27414     1578  20.9134     8
(1, 2, 8)     74.636  1.27062    14857  64.2924     8
(0, 6, 0)     75.257  1.26167     5645  79.6605     2
(3, 2, 0)     75.941  1.25199     1731  31.3669     4
(2, 1, 7)     76.253  1.24764       49   3.7495     8
(2, 4, 4)     77.017  1.23717     9168  51.4514     8
(3, 1, 3)     77.064  1.23652     2505  26.9052     8
(0, 6, 2)     77.385   1.2322     3221  43.2484     4
(3, 2, 2)     78.063  1.22318       22   2.5418     8
(1, 5, 5)     79.522  1.20437     8369  50.0509     8
(3, 0, 4)     79.547  1.20405     7173  65.5444     4
(1, 6, 0)     79.655  1.20268     7307  66.2001     4
(1, 1, 9)     79.995  1.19843     5323  40.0464     8
(2, 5, 1)     80.003  1.19833     2643  28.2226     8
(1, 6, 2)      81.75  1.17708     1478  21.3495     8
(3, 3, 1)      82.51  1.16815      173   7.3405     8
(2, 0, 8)     82.933  1.16327      230   12.006     4
(0, 6, 4)     83.675  1.15483     2133  36.6963     4
(2, 5, 3)     84.178   1.1492     5478  41.6966     8
(3, 2, 4)     84.342  1.14739    18806  77.3274     8
(0, 0, 10)     84.377    1.147     2311  54.2309     2
(0, 4, 8)     84.758  1.14282     9888  79.4834     4
(3, 1, 5)     85.438  1.13545     2847  30.2725     8
(2, 3, 7)     85.873  1.13081       26   2.9372     8
(3, 3, 3)     86.664  1.12251     1976  25.3883     8
(2, 4, 6)     87.463   1.1143     1469  21.9788     8
(2, 2, 8)     87.696  1.11194       19   2.5385     8
(1, 6, 4)      87.98  1.10908        5   1.3146     8
(1, 0, 10)     88.679  1.10214     3750  49.9421     4
(1, 4, 8)     89.058  1.09843     5535  42.9779     8
(0, 2, 10)     89.133   1.0977     6436  65.5665     4
(1, 3, 9)     89.538  1.09378     4404  38.4203     8
(3, 0, 6)     89.942  1.08992     3043  45.2455     4
(3, 4, 0)      90.32  1.08633     2761   43.173     4
(1, 5, 7)     91.984  1.07098      148   7.1284     8
(3, 4, 2)     92.388  1.06735      697  15.4716     8
(2, 5, 5)     92.458  1.06673     9539  57.2296     8
(2, 6, 0)      92.59  1.06556       42   5.4138     4
(2, 1, 9)     92.924   1.0626     4187  37.9849     8
(1, 2, 10)     93.427  1.05819     3559   35.087     8
(0, 6, 6)     94.028  1.05301      105   8.5492     4
(2, 6, 2)     94.661  1.04763     1583  23.5002     8
(3, 2, 6)     94.693  1.04736     2574  29.9657     8
(3, 3, 5)     94.941  1.04528     2334  28.5593     8
(1, 7, 1)     95.662   1.0393     1934  26.0606     8
(3, 1, 7)     97.872  1.02165      389  11.7725     8
(1, 6, 6)     98.347  1.01798       30   3.3116     8
(3, 4, 4)     98.622  1.01587     9037  56.7851     8
(1, 7, 3)     99.836  1.00676     2346  29.0178     8
(1, 1, 11)    100.782  0.99985      134   6.9675     8
(2, 6, 4)    100.924  0.99883     2375   29.256     8
(4, 0, 0)    101.459    0.995      329  21.8314     2
(3, 5, 1)    101.595  0.99404       90   5.7291     8
(2, 0, 10)    101.636  0.99375        3   1.5198     4
(2, 4, 8)    102.023  0.99103      284  10.1509     8
(2, 3, 9)    102.515  0.98761     3497  35.6017     8
(4, 1, 1)    103.203  0.98289     5147  43.2353     8
(0, 4, 10)    103.495   0.9809     3012  46.7904     4
(4, 0, 2)    103.577  0.98035        1   0.9127     4
(3, 0, 8)    104.569  0.97375     1380  31.7222     4
(2, 5, 7)    105.033  0.97072       29   3.2736     8
(3, 5, 3)    105.852  0.96546     1296  21.7589     8
(4, 2, 0)     106.35   0.9623     1504  33.1647     4
(2, 2, 10)    106.531  0.96117      462  13.0041     8
(0, 0, 12)    107.393  0.95583       28   6.4823     2
(4, 1, 3)    107.491  0.95523        4   1.3456     8
(3, 3, 7)    107.615  0.95447      337  11.1129     8
(1, 4, 10)    107.956  0.95241     5159   43.466     8
(1, 7, 5)     108.37  0.94992     4774  41.8162     8
(4, 2, 2)    108.517  0.94904      309  10.6547     8
(0, 6, 8)    108.833  0.94716     4317  56.2434     4
(1, 5, 9)    108.861  0.94699     3172  34.0921     8
(2, 7, 1)     108.87  0.94694     1624  24.3985     8
(0, 8, 0)    108.987  0.94625     3820  74.8235     2
(3, 4, 6)    109.288  0.94448     4054  38.5428     8
(3, 2, 8)    109.535  0.94305     1096  20.0463     8
(4, 0, 4)    110.059  0.94002     3557  51.0545     4
(1, 3, 11)    110.655  0.93662       96   5.9459     8
(0, 8, 2)    111.189  0.93363      618  21.2762     4
(2, 6, 6)     111.71  0.93074     2343  29.2828     8
(1, 0, 12)    111.952  0.92941       35    5.098     4
(0, 2, 12)    112.442  0.92674      445  18.0558     4
(4, 3, 1)    113.208  0.92264     4393  40.0555     8
(2, 7, 3)    113.302  0.92214     3179  34.0702     8
(1, 6, 8)    113.436  0.92143     6191  47.5425     8
(1, 8, 0)    113.596  0.92059     2254  40.5674     4
(2, 1, 11)     114.32  0.91682      322  10.8388     8
(3, 5, 5)    114.675  0.91499     1688  24.7922     8
(3, 6, 0)     114.82  0.91425      952  26.3372     4
(3, 1, 9)    115.189  0.91238       11   2.0654     8
(4, 2, 4)    115.203   0.9123     4110  38.6576     8
(1, 8, 2)    115.872  0.90895     2113  27.6923     8
(4, 1, 5)    116.416  0.90627      305  10.5174     8
(3, 6, 2)     117.12  0.90285       13   2.2002     8
(1, 2, 12)    117.172  0.90259       97   5.9298     8
(4, 3, 3)     117.79  0.89965        3   1.0429     8
(0, 8, 4)    118.014  0.89859      565  20.1913     4

The unfortunate thing about Abmm is that VESTA doesn't list values for U in the vesta console as it was listed for Acmm.

We are using the exact same atomic-scattering factor function and scattering parameters, debye-waller correction function, and the same lorentz polarization factor function. I believe that the only possible difference is the value U, I don't think that VESTA uses the values reported in their output file, just because of the oddity I observed in the Acmm structure. To restate it, VESTA's output file does not list a value U for one of the atoms, but the VESTA console does list a value that gives us okay agreement.

qzhu2017 / XRD

Discrepancy between our XRD and vesta #25