<div dir="ltr">What extrapolation method would you recommend for AIMD simulations if I use standard diagonalization and Broyden mixing?<div><br></div><div>In a lot of the CP2K examples, ASPC is recommended for AIMD simulations. </div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Jun 13, 2022 at 10:29 AM Marcella Iannuzzi <<a href="mailto:marci.akira@gmail.com">marci.akira@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div><br></div><div><br></div>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. <div>With standard diagonalization+Broyden mixing ASPC is also not really the best choice. </div><div>Regards</div><div>Marcella</div><div><br><br></div><div class="gmail_quote"><div dir="auto" class="gmail_attr">On Monday, June 13, 2022 at 10:17:59 AM UTC+2 <a href="mailto:lenardc...@gmail.com" target="_blank">lenardc...@gmail.com</a> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">It seems I am already using pretty much the best (fastest) settings possible then.<div>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.<br><br>Thank you very much Nick.</div></div><br><div class="gmail_quote"></div><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Jun 13, 2022 at 12:48 AM Nicholas Winner <<a rel="nofollow">nwi...@berkeley.edu</a>> wrote:<br></div></div><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">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.<div><br></div><div>Will not compromise accuracy (test all options to find best)</div><div><ol><li>Using traditional diagonalization:<ol><li>Use broyden mixer, which is generally (not always!) faster. </li><li>In broyden use as large of a alpha value as you can afford while still achieving convergence.</li></ol></li><li>Using OT:<ol><li>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.</li><li>if you know the system energy gap, you can supply a slight underestimate of it to the OT preconditioner to help it converge faster</li><li>Change from CG (which is more robust) to DIIS which is usually faster</li></ol></li><li>Use ASPC wfn extrapolation</li><li>Inspect your SCF convergence. If you see unstable long convergence, then increasing your precision settings might help you in the long run.</li></ol><div>Will/might compromise accuracy<br></div></div><div><ol><li>Decrease EPS_DEFAULT </li><li>Decrease EPS_SCF (tolerance for scf convergence)</li><li>Use UKS = False. I.e. spin unpolarized if you not need it.</li><li>Use a larger MD time-step. Some systems are still okay with a large time step of 2fs, could work for you.</li></ol><div>-Nick</div></div><div class="gmail_quote"><div dir="auto" class="gmail_attr">On Sunday, June 12, 2022 at 12:06:49 PM UTC-7 <a rel="nofollow">lenardc...@gmail.com</a> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Just replying to this to bump it higher since I haven't received a response yet.<br><br><div class="gmail_quote"><div dir="auto" class="gmail_attr">On Thursday, June 9, 2022 at 3:38:02 AM UTC+2 ASSIDUO Network wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">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. <br><br>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?<br><br>Key things to keep in mind for my work:<div>Using GTH-PBE potential and the DZVP-MOLOPT-SR-GTH basis set. Fermi dirac smearing must also be used </div></blockquote></div></blockquote></div>
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