I am having troubles in converging the low scaling ACFDT-RPA method for bulk Al. What happens is the following: As soon as I increase my K-point mesh to 10x10x10, I see the minimum excitation energy getting very small. As soon as OMEGAMIN (as detected automatically) gets smaller than 0.05 eV, the RPA correlation energy starts to give incorrect values. Increasing NOMEGA from 16 to 20 to 24 does not solve the problem:

Code: Select all

```
NOMEGA = 16
cutoff energy smooth cutoff RPA correlation Hartree contr. to MP2
---------------------------------------------------------------------------------
166.667 133.333 -1780.9308643871********************
158.730 126.984 -1781.0094794909********************
151.172 120.937 -1781.2266521184********************
143.973 115.178 -1781.2923937959********************
137.117 109.694 -1781.2340806750********************
130.588 104.470 -1781.2611010667********************
124.369 99.495 -1781.3385357701********************
118.447 94.758 -1781.3666223710********************
linear regression
converged value -1780.4551769844********************
NOMEGA = 20
cutoff energy smooth cutoff RPA correlation Hartree contr. to MP2
---------------------------------------------------------------------------------
166.667 133.333 -1841750.2556570359********************
158.730 126.984 -1841758.2503074536********************
151.172 120.937 -1841757.7727216494********************
143.973 115.178 -1841724.0818368408********************
137.117 109.694 -1841697.6935746986********************
130.588 104.470 -1841649.1188613100********************
124.369 99.495 -1841595.1484981293********************
118.447 94.758 -1841550.6474406589********************
linear regression
converged value -1842113.0359588007********************
```

Restricting OMEGAMIN to 0.05 eV in the INCAR does allow me to get reasonable values,

Code: Select all

```
NOMEGA = 16
cutoff energy smooth cutoff RPA correlation Hartree contr. to MP2
---------------------------------------------------------------------------------
166.667 133.333 -4.9300282298 -15.7623601002
158.730 126.984 -4.9228244905 -15.7544975990
151.172 120.937 -4.9150881364 -15.7461159963
143.973 115.178 -4.9064852493 -15.7371977502
137.117 109.694 -4.8976110150 -15.7276748460
130.588 104.470 -4.8878102895 -15.7174740140
124.369 99.495 -4.8775908195 -15.7065760995
118.447 94.758 -4.8665886704 -15.6948510066
linear regression
converged value -5.0249631892 -15.8629128458
```

It is obvious that there is still a problem, as the results get worse when increasing NOMEGA. However, I do not quite understand this behavior and I would be happy if someone could explain it to me (sorry, might be a very naive question...)

Is such a system "convergable" using the minimax method? If so, at which knobs do I need to turn?

(I should probably mention that the system converges nicely for NOMEGA>=16) when using the 4-step routine with the high scaling RPA method as was already implemented in VASP 5.)

Thank you for your help,

Best regards,

Katharina Doblhoff-Dier