ML LFAST: Difference between revisions

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{{DISPLAYTITLE:ML_LFAST}}
{{DISPLAYTITLE:ML_LFAST}}
{{TAGDEF|ML_LFAST|[logical]|.FALSE.}}
{{TAGDEF|ML_LFAST|[logical]}}
{{DEF|ML_LFAST|.TRUE.|for {{TAGO|ML_MODE|refit}}|.FALSE.|else}}


Description: This tag switches on the very fast execution mode for {{TAG|ML_ISTART}}=2.
Description: This tag switches on the descriptors for refitting in the fast execution mode within machine learning force fields. This tag is usually switched on by {{TAGO|ML_MODE|refit}} and doesn't need to be set explicitely.
{{NB|mind|This tag is only available as of VASP.6.4.0}}
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Until now this tag is only available in the development version of VASP.
This tag switches on the descriptors for the fast execution mode. To use the fast execution mode the force field first has to be refit with the fast descriptors. This is done by setting {{TAGO|ML_MODE|refit}}, which will automatically set {{TAG|ML_LFAST|.TRUE.}}. As usual, the resulting {{TAG|ML_FFN}} has to be copied to {{TAG|ML_FF}} and the fast code will be automatically run in production mode runs by setting {{TAG|ML_MODE|run}}.


This tag significantly speeds up the calculation for {{TAG|ML_ISTART}}=2, but without the Bayesian error prediction.
The speedup of the fast method compared to the regular method is increasing with increasing number of local reference configurations.
One can still have Bayesian error prediction by carrying out the slow version every <code>n</code> steps, by setting
 
{{TAG|ML_IERR}}=n. But by default this is omitted.
It should be noted that in the fast version no Bayesian error estimation is available.
 
Since the calculation time of the fast version is of the same order of magnitude as the timing for the output of the molecular-dynamics results, we advise decreasing the output frequency for molecular dynamics by the tag {{TAG|ML_OUTBLOCK}}. Additionally, the calculation and output of the pair-correlation function can be suppressed by {{TAGO|ML_OUTPUT_MODE|0}} as default. {{NB|deprecated|If {{TAGO|ML_LFAST|.TRUE.}} is explicitely used without the {{TAG|ML_MODE}} tag then it can only be used with {{TAGO|ML_ISTART|4}} and {{TAGO|ML_IALGO_LINREG|1|op=>}}.}}


== Related tags and articles ==
== Related tags and articles ==
{{TAG|ML_LMLFF}}, {{TAG|ML_ISTART}}, {{TAG|ML_IERR}}
{{TAG|ML_LMLFF}}, {{TAG|ML_MODE}}, {{TAG|ML_IERR}}, {{TAG|ML_OUTBLOCK}}, {{TAG|ML_OUTPUT_MODE}}
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[[Category:INCAR tag]][[Category:Machine-learned force fields]][[Category:Development version]]
[[Category:INCAR tag]][[Category:Machine-learned force fields]]

Latest revision as of 15:30, 19 October 2023

ML_LFAST = [logical] 

Default: ML_LFAST = .TRUE. for ML_MODE = refit
= .FALSE. else

Description: This tag switches on the descriptors for refitting in the fast execution mode within machine learning force fields. This tag is usually switched on by ML_MODE = refit and doesn't need to be set explicitely.

Mind: This tag is only available as of VASP.6.4.0

This tag switches on the descriptors for the fast execution mode. To use the fast execution mode the force field first has to be refit with the fast descriptors. This is done by setting ML_MODE = refit, which will automatically set ML_LFAST. As usual, the resulting ML_FFN has to be copied to ML_FF and the fast code will be automatically run in production mode runs by setting ML_MODE.

The speedup of the fast method compared to the regular method is increasing with increasing number of local reference configurations.

It should be noted that in the fast version no Bayesian error estimation is available.

Since the calculation time of the fast version is of the same order of magnitude as the timing for the output of the molecular-dynamics results, we advise decreasing the output frequency for molecular dynamics by the tag ML_OUTBLOCK. Additionally, the calculation and output of the pair-correlation function can be suppressed by ML_OUTPUT_MODE = 0 as default.

Deprecated: If ML_LFAST = .TRUE. is explicitely used without the ML_MODE tag then it can only be used with ML_ISTART = 4 and ML_IALGO_LINREG > 1.

Related tags and articles

ML_LMLFF, ML_MODE, ML_IERR, ML_OUTBLOCK, ML_OUTPUT_MODE