[CP2K-user] [CP2K:17140] Re: "Fastest" AIMD simulation settings/parameters

[Lenard Carroll]

What extrapolation method would you recommend for AIMD simulations if I use
standard diagonalization and Broyden mixing?

In a lot of the CP2K examples, ASPC is recommended for AIMD simulations.

On Mon, Jun 13, 2022 at 10:29 AM Marcella Iannuzzi <marci.akira at gmail.com>
wrote:

>
>
> If you are using Broyden mixing, the optimisation of the wfn is most
> probably based on standard diagonalization, hence no preconditioner is
> applied and neither CG nor DIIS as well.
> With standard diagonalization+Broyden mixing ASPC is also not really the
> best choice.
> Regards
> Marcella
>
>
> On Monday, June 13, 2022 at 10:17:59 AM UTC+2 lenardc... at gmail.com wrote:
>
>> It seems I am already using pretty much the best (fastest) settings
>> possible then.
>> For my AIMD simulations I do use Broyden Mixing, with a large-ish alpha
>> value, FULL_KINETIC predconditioner, ASPC extrapolation method, no UKS and
>> a 2 fs time step. I am, however, using CG for the minimizer instead of
>> DIIS. I am going to test that out and see how it looks.
>>
>> Thank you very much Nick.
>>
>> On Mon, Jun 13, 2022 at 12:48 AM Nicholas Winner <nwi... at berkeley.edu>
>> wrote:
>>
>>> Here's a list of some things I can think of to increase speed. Probably
>>> not comprehensive. In the future include your input file for people so we
>>> can see what settings you are already trying.
>>>
>>> Will not compromise accuracy (test all options to find best)
>>>
>>>    1. Using traditional diagonalization:
>>>       1. Use broyden mixer, which is generally (not always!) faster.
>>>       2. In broyden use as large of a alpha value as you can afford
>>>       while still achieving convergence.
>>>    2. Using OT:
>>>       1. Change preconditioner to speed up the scf convergence.
>>>       Normally Full_all or Full_single_inverse are most effective, but
>>>       full_kinetic might be good if you have a huge system that takes too long to
>>>       precondition.
>>>       2. if you know the system energy gap, you can supply a slight
>>>       underestimate of it to the OT preconditioner to help it converge faster
>>>       3. Change from CG (which is more robust) to DIIS which is usually
>>>       faster
>>>    3. Use ASPC wfn extrapolation
>>>    4. Inspect your SCF convergence. If you see unstable long
>>>    convergence, then increasing your precision settings might help you in the
>>>    long run.
>>>
>>> Will/might compromise accuracy
>>>
>>>    1. Decrease EPS_DEFAULT
>>>    2. Decrease EPS_SCF (tolerance for scf convergence)
>>>    3. Use UKS = False. I.e. spin unpolarized if you not need it.
>>>    4. Use a larger MD time-step. Some systems are still okay with a
>>>    large time step of 2fs, could work for you.
>>>
>>> -Nick
>>> On Sunday, June 12, 2022 at 12:06:49 PM UTC-7 lenardc... at gmail.com
>>> wrote:
>>>
>>>> Just replying to this to bump it higher since I haven't received a
>>>> response yet.
>>>>
>>>> On Thursday, June 9, 2022 at 3:38:02 AM UTC+2 ASSIDUO Network wrote:
>>>>
>>>>> Hi there, I have been running AIMD simulations for about a year now
>>>>> and it originally started off really slow, only getting 60 fs per day. In
>>>>> particular, I used the NVT ensemble and the Nose-Hoover thermostat. When
>>>>> swapping to the Langevin ensemble and adaptive-Langevin thermostat, the
>>>>> simulation time improved to 200-600 fs a day.
>>>>>
>>>>> I wanted to know what settings/parameters you would suggest to improve
>>>>> the speed even further? That is, what are the best settings to choose to
>>>>> get the fastest performance?
>>>>>
>>>>> Key things to keep in mind for my work:
>>>>> Using GTH-PBE potential and the DZVP-MOLOPT-SR-GTH basis set. Fermi
>>>>> dirac smearing must also be used
>>>>>
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