Recent Posts

Pages: [1] 2 3 ... 10

thanks a lot for those really useful tests and findings!

The SCF integral thresholds which are derived from $scfconv settings do take care of the number of basis sets already, and they are also adapted depending on the property which is being calculated. Also the 'diffuseness' can be important and is partly checked.

But it is really unusual to have those problems with the gradients already. 2nd derivatives, NMR, etc. is not unexpected, but gradients...

Is it a very delocalized system? But rocksalts are semiconductors.

The interesting question is: Where do the numerical problems come from? From which part - DFT functional, Exchange, Coulomb, ...?
But to answer that and to potentially find out where to change the defaults, it is necessary to get those contributions separately.

I therefore would like to ask you to send the input files to the support team!

Best Regards,
Thanks Antti. No this straight, unadulterated DFT using dscf/grad. Might have to switch to ridft+rdgrad though if I would want to look at even larger particles.


Thanks a lot for the update Martijn, this was interesting information. Are you using ridft+rdgrad for your calculations?

Thanks Antti & Marek, I did some testing using smaller basis-sets to speed up calculations:


B3LYP+D3(BJ)/def-SV(P)/scfconv=7/m5 -> geometry optimisation converges without problems.
B3LYP+D3(BJ)/def2-SVP/scfconv=7/m5 -> geometry optimisation does not converge. Gradient get stuck. Trust radius shrinks to zero.

This made me wonder if there was an issue with the integral screening tolerance, also because that changes with the basis-set size, so I manual set $scftol to 1E-16:

B3LYP+D3(BJ)/def2-SVP/scfconv=7/m5/scftol=1E-16 -> geometry optimisation does not converge. Gradient get stuck. Trust radius shrinks to zero.

In a last ditch attempt, I increased the SCF tolerance from 7 to 8:

B3LYP+D3(BJ)/def2-SVP/scfconv=8/m5/scftol=1E-16 -> geometry optimisation converges without problems.

For smaller particles scfconv 7 or even 6 works fine in terms of geometry optimisation. The effect of changing the scf tolerance on SCF energies of the large particles is negligible:

B3LYP+D3(BJ)/def2-SVP/scfconv=7/m5/scftol=1E-16 -> SCF energy =   -29733.7250655300   |dE/dxyz| =  0.119037
B3LYP+D3(BJ)/def2-SVP/scfconv=8/m5/scftol=1E-16 -> SCF energy =   -29733.7250703000   |dE/dxyz| =  0.036580

but the gradient for this large particles is clearly very sensitive to the scf tolerance value. Much more sensitive than i expected.

I'll now try to repeat the def2-TZVPP calculation using scfconv=8.


Escf and Egrad / unreliable spectra with radless
« Last post by hd1055 on November 18, 2020, 11:30:25 AM »
Dear Turbomole-Team,

We have recently used Turbomole7.4 in our group in order to generate absorption and emission spectra. For that, we have applied the following procedure:

1) Perform a geometry optimization for the molecule in the electronic ground state (S0)
2) Compute the vertical excitation energy for the transition S0 -> S1
3) Perform a geometry optimization for S1
4) Compute the vertical deexcitation energy for S1 -> S0 transition
5) Perform a normal mode analysis at optimized geometry of S0
6) Perform a normal mode analysis at optimized geometry of S1

Before using the program radless, we ensured that no imaginary frequencies appeared. We then prepared the control and control-gs files (and other files) as described in the user manual. There, we used the following parameters:

$spectral width 4
$delta_t 0.25
$max time 131072
$lifetime XXX

where XXX is the lifetime in atomic units (we used values corresponding to up to 40 femtoseconds). In many cases, very good results were achieved with this setup. However, for about 50% of the molecules we have tested, the emission spectrum assumed a shape that appears unreliable to us. We therefore varied the radless-parameters given above. Particulary, we focused on the spectral width and tested values of 1 to 100, but we did not find any improvement. We then checked the geometric displacements listed in the standard output of radless and extracted the maximum value. We find that the larger the displacement the likely radless will result in an unphysical spectrum.

It is not clear to us why these displacements should be so large (values of up to 3000 were observed), because the S1 geometries are not much different from their respective S0 counterparts. We found a hint in the literature that contaminations due to an axis flip can result in large geometric displacements because normal modes of ground and excited states may then be defined in different coordinate systems.

We would like to know if such a contamination can occur in Turbmole's radless program or if this has been compensated for. Are there other reasons I have not mentioned here which may lead to unphysical vibronic spectra?

Thank you in advance for your support.
Hi Martijn,

the trust radius shrinking to the minimum usually indicates that the PES is not smooth enough. In large systems, the reason for this may be too small DFT grid or SCF thresholds that are too loose.

Best, Marek
Hi Martijn,

sounds like a tricky problem indeed. I don't have any solid advice, but at least some thoughts:

1) You mention that the trust radius shrinks to the minimum value. What if you increase the minimum trust radius (statpt option "radmin") to force the optimization algorithm to take larger steps? Note that this might also be a bad idea that removes even the slow convergence behavior that you see at the moment :D.

2) Have you tried optimization in cartesian coordinates? For "normal" molecules, redundant internals practically always yield better performance, but maybe this kind of rocksalt nanostructure is not that great for redundant internals, especially if you have a very ionic scenario. Similar to my comment (1), switching to cartesian coordinates may eventually lead to even worse behavior, but could be worth checking out.


I have this weird problem. I'm using Turbomole 7.5 to optimise the geometry of rocksalt nanoparticles using B3LYP+D3/TZVPP and for particles with roughly 100 atoms that works absolutely fine. Jobex + STATPT converges without too much problems to a minimum.

For larger particles, containing ~200 atoms, however, things become problematic. Convergence becomes tediously slow, the trust radius shrinks to the minimum and after hundreds of steps the gradient is typically only slightly smaller and the energy slightly more negative. All this while the structure of these nanoparticles is very similar to the smaller ones where the convergence is fine.

The other issues is that a number of issues we optimised similar size nanoparticles with a similar set-up, other than that we didn't use the D3 dispersion correction at the time and used a 6.x version of Turbomole, and if memory serves me well we never encountered these sorts of problems.

Having tried seemingly anything, smaller basis-sets, no dispersion correction, using relax rather than statpt, I'm stuck. Am I just unlucky or is there some trick to optimising these very large structures?


Escf and Egrad / Re: Analysis of BSE excitations
« Last post by martijn on November 07, 2020, 02:13:39 AM »
Thanks Uwe & Christof. That's very useful.

Escf and Egrad / Re: Analysis of BSE excitations
« Last post by chris.hol on November 03, 2020, 10:40:37 PM »
Hi Martijn,

1) Yes, the GW/BSE oscillator strength and other properties are meaningful. Use them just like the TD-DFT ones. Actually, the oscillator strengths seem to be rather good (see J. Chem. Theory Comput. 2016, 12, 8, 3969–3981; also we found the lifetimes in 2c GW/BSE calculations to be often better than their TD-DFT counterparts). However, GW/BSE oscillator strengthts do not obey the sum rules as TD-DFT (so velocity/length representations will not coincide for a complete basis set), if one is very strict about these things.

2) As Uwe said, you can visualize NTOs just like TD-DFT using the proper program, there select the mos / dftnto menu. the dftnto menu will actually recognize the $bse flag and report something like "found $bse flag, assuming BSE response calculation". The transition density of JCC 2017, 38, 383 can be obtained from the "panama" script, which should also be compatible with GW/BSE (we recommend the use of NTOs though)

All the best
Pages: [1] 2 3 ... 10