[Wien] [SPAM?] same k vector, different eigenvectors
曹迎迎
yycao at iphy.ac.cn
Thu Apr 20 11:07:30 CEST 2023
Dear all,
I am runing a one-step calculation to get the eigenvector of ferromagnetic BCC-Fe at k=(1,1,1) from a self-consistent charge density. The command used is ' runsp_lapw -so -p -s lapw1 -e lapwso'.
First i get the self-consistent charge density by using 'runsp_lapw -ec 0.0001 -cc 0.0001 -p -i 100 -so'. Then i copy the scf results to two directories and change the klist respectively. In one directory, i use only two k-points (0,0,0) and (1,1,1) in klist as:
1 0 0 0 1 1.0 -7.0 1.5 1 k, div: ( 1 1 1)
2 10 10 10 10 1.0
END,
and in another directory i use101 k-points including (1,1,1) as:
...
99 10 9 9 10 8.0
100 10 8 10 10 4.0
101 10 10 10 10 1.0
END.
As a consequence, I get two results about eigenvectors at k=(1,1,1) in these two directory. I find that the two results are not identical. In the 2-kpts case, the lowest eigenvalue and eigenvector in vectorsoup file is:
1 -5.83325860040616
-0.149115553408975 5.906541789778055E-002 -0.149115710346805
5.906548006164127E-002 -0.149115710346904 5.906548006168073E-002
-0.149115710346889 5.906548006167468E-002 -0.149115710346984
5.906548006171229E-002 -0.149115553409014 5.906541789779599E-002
...,
while in the 101-kpts case, it is:
1 -5.83325860040616
-2.301711645066824E-004 0.160387402501481 -2.301714067522111E-004
0.160387571302459 -2.301714067523196E-004 0.160387571302571
-2.301714067523697E-004 0.160387571302556 -2.301714067524974E-004
0.160387571302664 -2.301711645069168E-004 0.160387402501536
....
In my understanding, the hamiltonian is only k-dependent but not k-mesh denpendent. So i should get the same hamiltonian in the two directories at same k-point, so as the eigenvector. Did i do something wrong? Or what misunderstanding do i have? If not, why are not the eigenvectors exactly the same?
I am wondering if anyone has encountered a similar issue or has any insights on why this may be happening. Any suggestions or advise would be greatly appreciated.
Thank you in advance.
Best regards, Yingying Cao
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