Author Topic: Optimising the geometry of very large nanoparticles  (Read 161 times)

martijn

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Optimising the geometry of very large nanoparticles
« on: November 07, 2020, 02:38:39 AM »
Hi,

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?

Thanks,

Martijn

antti_karttunen

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Re: Optimising the geometry of very large nanoparticles
« Reply #1 on: November 07, 2020, 12:25:03 PM »
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.

Best,
Antti

Marek Sierka

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Re: Optimising the geometry of very large nanoparticles
« Reply #2 on: November 08, 2020, 06:51:35 PM »
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

martijn

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Re: Optimising the geometry of very large nanoparticles
« Reply #3 on: November 23, 2020, 09:51:36 PM »
Thanks Antti & Marek, I did some testing using smaller basis-sets to speed up calculations:

First:

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.

Best,

Martijn

antti_karttunen

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Re: Optimising the geometry of very large nanoparticles
« Reply #4 on: November 24, 2020, 05:44:17 PM »
Thanks a lot for the update Martijn, this was interesting information. Are you using ridft+rdgrad for your calculations?

Best,
Antti

martijn

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Re: Optimising the geometry of very large nanoparticles
« Reply #5 on: November 24, 2020, 11:31:28 PM »
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.

Best,

Martijn