ML CDOUB: Difference between revisions

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{{TAGDEF|ML_FF_CDOUB|[real]|2.0}}
{{TAGDEF|ML_FF_CDOUB|[real]|2.0}}


Description:
Description: This flag controls the necessity of DFT calculations in the machine learning force field method
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This flag is applied in the case of {{TAG|ML_FF_ISAMPLE}}=2. In that case if the estimated error is {{TAG|ML_FF_CDOUB}} times larger than the threshold ({{TAG|ML_FF_CSF}} for the spilling factor and {{TAG|ML_FF_CTIFOR}} or it's updated value form {{TAG|ML_FF_LCRITERIA}}=''.TRUE.'' for the Bayesian error), the sampling is stopped and a new force field is generated.


== Related Tags and Sections ==
== Related Tags and Sections ==
{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_ISAMPLE}}, {{TAG|ML_FF_CSF}}, {{TAG|ML_FF_CTIFOR}}, {{TAG|ML_FF_CTIFOR}}


{{sc|ML_FF_CDOUB|Examples|Examples that use this tag}}
{{sc|ML_FF_CDOUB|Examples|Examples that use this tag}}

Revision as of 13:00, 13 May 2019

ML_FF_CDOUB = [real]
Default: ML_FF_CDOUB = 2.0 

Description: This flag controls the necessity of DFT calculations in the machine learning force field method


This flag is applied in the case of ML_FF_ISAMPLE=2. In that case if the estimated error is ML_FF_CDOUB times larger than the threshold (ML_FF_CSF for the spilling factor and ML_FF_CTIFOR or it's updated value form ML_FF_LCRITERIA=.TRUE. for the Bayesian error), the sampling is stopped and a new force field is generated.

Related Tags and Sections

ML_FF_LMLFF, ML_FF_ISAMPLE, ML_FF_CSF, ML_FF_CTIFOR, ML_FF_CTIFOR

Examples that use this tag