ML IERR: Difference between revisions
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{{DISPLAYTITLE:ML_IERR}} | {{DISPLAYTITLE:ML_IERR}} | ||
{{DEF|ML_IERR|0|if {{TAG|ML_ISTART}}{{=}}2|1|otherwise}} | {{DEF|ML_IERR|0|if {{TAG|ML_ISTART}}{{=}}2|always 1|otherwise}} | ||
Description: Calculation frequency of Bayesian error estimate. | Description: Calculation and output frequency of Bayesian error estimate. | ||
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This tag | This tag sets the distance of molecular-dynamics steps for which the Bayesian error estimates are calculated and written to the {{TAG|ML_LOGFILE}} (corresponding to the keyword <code>BEE</code> and <code>BEEF</code>). If learning is activated ({{TAG|ML_ISTART}}=0,1 or 2) Bayesian error estimation must be "on" at every molecular-dynamics step ({{TAG|ML_IERR}}=1 is required). | ||
Only for {{TAG|ML_ISTART}}=2 can {{TAG|ML_IERR}} be freely chosen. For {{TAG|ML_IERR}}=0 the Bayesian error is never calculated (and also no line is written out). This is the default since for {{TAG|ML_ISTART}}=2 the Bayesian error estimation takes up a significant amount of the total calculation time for each step. | |||
{{TAG|ML_IERR}}>0 can be also used with the fast execution mode {{TAG|ML_LFAST}}=.TRUE., but at each step where the Bayesian error estimation is carried out, the code has to switch to the slow version. | |||
== Related tags and articles == | == Related tags and articles == | ||
Revision as of 11:41, 21 November 2022
| Default: ML_IERR | = 0 | if ML_ISTART=2 |
| = always 1 | otherwise |
Description: Calculation and output frequency of Bayesian error estimate.
This tag sets the distance of molecular-dynamics steps for which the Bayesian error estimates are calculated and written to the ML_LOGFILE (corresponding to the keyword BEE and BEEF). If learning is activated (ML_ISTART=0,1 or 2) Bayesian error estimation must be "on" at every molecular-dynamics step (ML_IERR=1 is required).
Only for ML_ISTART=2 can ML_IERR be freely chosen. For ML_IERR=0 the Bayesian error is never calculated (and also no line is written out). This is the default since for ML_ISTART=2 the Bayesian error estimation takes up a significant amount of the total calculation time for each step.
ML_IERR>0 can be also used with the fast execution mode ML_LFAST=.TRUE., but at each step where the Bayesian error estimation is carried out, the code has to switch to the slow version.
Related tags and articles
ML_LMLFF, ML_ISTART, ML_LFAST, ML_OUTBLOCK, ML_OUTPUT_MODE