Mcsqs for pyrochlore structure

Dear Prof. Dr. A. van de Walle,
I want to get CaCeTi2O7 pyrochlore structure( 88 atoms ), cif file like that


CaCeTi2O7.cif
Spacegroup: F d -3 m
a: 10.211 alpha:90
b: 10.211 beta:90
c: 10.211 gamma:90
Site Wychoff X Y Z Occ
O1 48f 0.4286 0.1250 0.1250 1.0
Ti 16d 0.5000 0.5000 0.5000 1.0
Ca 16c 0.0000 0.0000 0.0000 0.5
Ce 16c 0.0000 0.0000 0.0000 0.5
O2 8a 0.1250 0.1250 0.1250 1.0


It has 88 atoms ( Ca8Ce8Ti16 O56) in unit cell, like that


rndstr_88 atoms.in
10.211 10.211 10.211 90 90 90
1 0 0
0 1 0
0 0 1
0.000000000 0.000000000 0.000000000 Ca=0.5,Ce=0.5
0.750000000 0.250000000 0.500000000 Ca=0.5,Ce=0.5
0.250000000 0.750000000 0.500000000 Ca=0.5,Ce=0.5
0.250000000 0.500000000 0.750000000 Ca=0.5,Ce=0.5
0.750000000 0.500000000 0.250000000 Ca=0.5,Ce=0.5
0.500000000 0.750000000 0.250000000 Ca=0.5,Ce=0.5
0.500000000 0.250000000 0.750000000 Ca=0.5,Ce=0.5
0.000000000 0.500000000 0.500000000 Ca=0.5,Ce=0.5
0.750000000 0.750000000 0.000000000 Ca=0.5,Ce=0.5
0.250000000 0.250000000 0.000000000 Ca=0.5,Ce=0.5
0.250000000 0.000000000 0.250000000 Ca=0.5,Ce=0.5
0.750000000 0.000000000 0.750000000 Ca=0.5,Ce=0.5
0.500000000 0.000000000 0.500000000 Ca=0.5,Ce=0.5
0.000000000 0.750000000 0.750000000 Ca=0.5,Ce=0.5
0.000000000 0.250000000 0.250000000 Ca=0.5,Ce=0.5
0.500000000 0.500000000 0.000000000 Ca=0.5,Ce=0.5
0.500000000 0.500000000 0.500000000 Ti
0.250000000 0.750000000 0.000000000 Ti
0.750000000 0.250000000 0.000000000 Ti
0.750000000 0.000000000 0.250000000 Ti
0.250000000 0.000000000 0.750000000 Ti
0.000000000 0.250000000 0.750000000 Ti
0.000000000 0.750000000 0.250000000 Ti
0.500000000 0.000000000 0.000000000 Ti
0.250000000 0.250000000 0.500000000 Ti
0.750000000 0.750000000 0.500000000 Ti
0.750000000 0.500000000 0.750000000 Ti
0.250000000 0.500000000 0.250000000 Ti
0.000000000 0.500000000 0.000000000 Ti
0.500000000 0.250000000 0.250000000 Ti
0.500000000 0.750000000 0.750000000 Ti
0.000000000 0.000000000 0.500000000 Ti
0.125000000 0.125000000 0.125000000 O
0.875000000 0.875000000 0.875000000 O
0.625000000 0.125000000 0.625000000 O
0.375000000 0.875000000 0.375000000 O
0.125000000 0.625000000 0.625000000 O
0.875000000 0.375000000 0.375000000 O
0.625000000 0.625000000 0.125000000 O
0.375000000 0.375000000 0.875000000 O
0.428600013 0.125000000 0.125000000 O
0.571399987 0.875000000 0.875000000 O
0.321399987 0.125000000 0.625000000 O
0.678600013 0.875000000 0.375000000 O
0.821399987 0.625000000 0.625000000 O
0.178600013 0.375000000 0.375000000 O
0.928600013 0.625000000 0.125000000 O
0.071399987 0.375000000 0.875000000 O
0.125000000 0.428600013 0.125000000 O
0.875000000 0.571399987 0.875000000 O
0.625000000 0.321399987 0.125000000 O
0.375000000 0.678600013 0.875000000 O
0.625000000 0.821399987 0.625000000 O
0.375000000 0.178600013 0.375000000 O
0.125000000 0.928600013 0.625000000 O
0.875000000 0.071399987 0.375000000 O
0.125000000 0.125000000 0.428600013 O
0.875000000 0.875000000 0.571399987 O
0.125000000 0.625000000 0.321399987 O
0.875000000 0.375000000 0.678600013 O
0.625000000 0.625000000 0.821399987 O
0.375000000 0.375000000 0.178600013 O
0.625000000 0.125000000 0.928600013 O
0.375000000 0.875000000 0.071399987 O
0.875000000 0.678600013 0.375000000 O
0.125000000 0.321399987 0.625000000 O
0.375000000 0.071399987 0.875000000 O
0.625000000 0.928600013 0.125000000 O
0.071399987 0.875000000 0.375000000 O
0.928600013 0.125000000 0.625000000 O
0.678600013 0.375000000 0.875000000 O
0.321399987 0.625000000 0.125000000 O
0.875000000 0.375000000 0.071399987 O
0.125000000 0.625000000 0.928600013 O
0.375000000 0.875000000 0.678600013 O
0.625000000 0.125000000 0.321399987 O
0.428600013 0.625000000 0.625000000 O
0.571399987 0.375000000 0.375000000 O
0.821399987 0.125000000 0.125000000 O
0.178600013 0.875000000 0.875000000 O
0.125000000 0.125000000 0.821399987 O
0.875000000 0.875000000 0.178600013 O
0.625000000 0.625000000 0.428600013 O
0.375000000 0.375000000 0.571399987 O
0.875000000 0.178600013 0.875000000 O
0.125000000 0.821399987 0.125000000 O
0.375000000 0.571399987 0.375000000 O
0.625000000 0.428600013 0.625000000 O


But the rndstr_88atoms.in was not primitive unit cell, so i used the cellcvrt command to make rndstr.in (22 atoms), like that


rndstr.in
10.211000 0.000000 0.000000
0.000000 10.211000 0.000000
0.000000 0.000000 10.211000
0.000000 0.500000 -0.500000
0.500000 0.500000 0.000000
0.500000 0.000000 -0.500000
1.000000 1.000000 -1.000000 Ca=0.5,Ce=0.5
0.750000 0.750000 -1.000000 Ca=0.5,Ce=0.5
0.750000 0.500000 -0.750000 Ca=0.5,Ce=0.5
0.500000 0.750000 -0.750000 Ca=0.5,Ce=0.5
0.500000 0.500000 -0.500000 Ti
0.750000 0.750000 -0.500000 Ti
0.750000 1.000000 -0.750000 Ti
1.000000 0.750000 -0.750000 Ti
0.625000 0.625000 -0.875000 O
0.375000 0.375000 -0.125000 O
0.428600 0.625000 -0.375000 O
0.571400 0.375000 -0.625000 O
0.321400 0.125000 -0.375000 O
0.678600 0.875000 -0.625000 O
0.625000 0.428600 -0.375000 O
0.375000 0.571400 -0.625000 O
0.125000 0.321400 -0.375000 O
0.875000 0.678600 -0.625000 O
0.625000 0.625000 -0.571400 O
0.375000 0.375000 -0.428600 O
0.125000 0.125000 -0.178600 O
0.875000 0.875000 -0.821400 O


And I made the sqscell.out file


1
1.000000 0.000000 0.000000
0.000000 1.000000 0.000000
0.000000 0.000000 1.000000


Then I used corrdump -l=rndstr.in -ro -noe -nop -clus -2=1…12 and mcsqs -rc -n=88, the results like


-2=4,5,6

bestcorr.out
2 3.610134 0.000000 0.000000 0.000000
Objective_function= Perfect_match


-2=7

bestcorr.out
2 3.610134 0.000000 0.000000 0.000000
2 6.252935 0.000000 0.000000 0.000000
2 7.220267 -0.333333 0.000000 -0.333333
2 7.220267 -0.333333 0.000000 -0.333333
2 8.072504 0.000000 0.000000 0.000000
Objective_function= -1.777778


-2=8

bestcorr.out
2 3.610134 0.000000 0.000000 0.000000
2 6.252935 -0.166667 0.000000 -0.166667
2 7.220267 0.000000 0.000000 0.000000
2 7.220267 0.000000 0.000000 0.000000
Objective_function= -1.676495


-2=9

bestcorr.out
2 3.610134 0.000000 0.000000 0.000000
2 6.252935 0.000000 0.000000 0.000000
2 7.220267 -0.333333 0.000000 -0.333333
2 7.220267 -0.333333 0.000000 -0.333333
2 8.072504 0.000000 0.000000 0.000000
Objective_function= -1.777778


-2=10

bestcorr.out
2 3.610134 0.000000 0.000000 0.000000
2 6.252935 0.000000 0.000000 0.000000
2 7.220267 -0.333333 0.000000 -0.333333
2 7.220267 -0.333333 0.000000 -0.333333
2 8.072504 0.000000 0.000000 0.000000
2 9.551516 0.000000 0.000000 0.000000
Objective_function= -1.833333


-2=11

bestcorr.out
2 3.610134 0.000000 0.000000 0.000000
2 6.252935 0.000000 0.000000 0.000000
2 7.220267 -0.333333 0.000000 -0.333333
2 7.220267 -0.333333 0.000000 -0.333333
2 8.072504 0.000000 0.000000 0.000000
2 9.551516 0.000000 0.000000 0.000000
2 10.211000 1.000000 0.000000 1.000000
2 10.830401 0.000000 0.000000 0.000000
2 10.830401 0.000000 0.000000 0.000000
Objective_function= -1.761905


-2=12

bestcorr.out
2 3.610134 0.000000 0.000000 0.000000
2 6.252935 0.000000 0.000000 0.000000
2 7.220267 -0.333333 0.000000 -0.333333
2 7.220267 -0.333333 0.000000 -0.333333
2 8.072504 0.000000 0.000000 0.000000
2 9.551516 0.000000 0.000000 0.000000
2 10.211000 1.000000 0.000000 1.000000
2 10.830401 0.000000 0.000000 0.000000
2 10.830401 0.000000 0.000000 0.000000
2 11.973459 0.000000 0.000000 0.000000
Objective_function= -1.791667


So, my questions are:
1 Are my rndstr.in and sqscell.out files right? (I want to get the best sqs for unit cell,1M-CM-^W1M-CM-^W1,88 atoms, and the ATAT is 3.16)
2 When i used -2=4,5,6, the result was `Objective_function= Perfect_match’, was it the best SQS? Or which bestcorr.out was the best when -2=4…12?
3 When i used bigger values -2=…,-3=…,-4=…, like
corrdump -l=rndstr.in -ro -noe -nop -clus -2=9 -3=8 -4=8
the bestcorr.out file looks like below, and whatever I changed the value(-2=…,-3=…,-4=…), the last column value in bestcorr.out always be 0.0000 -0.3333 1.0000, it would not become smaller. So,if I want to get better sqs, how to do next, Do you have any suggestions?
4 If I used bigger supercell(2M-CM-^W2M-CM-^W2), the last column value in bestcorr.out would be smaller, but computational cost will be increased in vasp, so I still want to get the best sqs for unit cell(1M-CM-^W1M-CM-^W1,88 atoms). How to solve this problem?


bestcorr.out
corrdump -l=rndstr.in -ro -noe -nop -clus -2=9 -3=8 -4=8
mcsqs -rc -n=88
2 3.610134 0.000000 0.000000 0.000000
2 6.252935 0.000000 0.000000 0.000000
2 7.220267 -0.333333 0.000000 -0.333333
2 7.220267 -0.333333 0.000000 -0.333333
2 8.072504 0.000000 0.000000 0.000000
3 3.610134 0.000000 0.000000 0.000000
3 6.252935 0.000000 0.000000 0.000000
3 6.252935 0.000000 0.000000 0.000000
3 6.252935 0.000000 0.000000 0.000000
3 7.220267 0.000000 0.000000 0.000000
3 7.220267 0.000000 0.000000 0.000000
3 7.220267 0.000000 0.000000 0.000000
3 7.220267 0.000000 0.000000 0.000000
3 7.220267 0.000000 0.000000 0.000000
3 7.220267 0.000000 0.000000 0.000000
4 3.610134 1.000000 0.000000 1.000000
4 6.252935 -0.333333 0.000000 -0.333333
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 -0.333333 0.000000 -0.333333
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 1.000000 0.000000 1.000000
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 -0.333333 0.000000 -0.333333
4 7.220267 0.000000 0.000000 0.000000
4 7.220267 0.000000 0.000000 0.000000
Objective_function= -4.873563


Thank you for your patiences
Best Regards,
Li

Hi Li,

I’m going to try and answer some of these, since you seem to be doing the same thing I am (with a different material). Can I ask first, what you are going to do with the material - I ask because you are only including pairs, however you will likely get a more accurate answer including triplets, and quadruplets.

  1. No obvious errors in the input files.
  2. `Objective_function= Perfect_match’ means that the code was able to match all the correlations you specified. If you specify even more correlations you may get s better SQS even if the code does not indicate "Perfect_match". You have the look and analyze the bestcorr.out file.
  3. You need to consider a bigger supercell if you want more correlations to be matched perfectly.
  4. Sorry a bigger cell is your only option to get a better SQS.