ML SIGW0: Difference between revisions

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{{TAGDEF|ML_FF_SIGW0_MB|[real]|1.0}}
{{DISPLAYTITLE:ML_SIGW0}}
{{TAGDEF|ML_SIGW0|[real]|}}
{{DEF|ML_SIGW0|1E-7|for {{TAG|ML_MODE}} {{=}} REFIT|1.0|else}}


Description: This flag sets the initial reversed and squared precision parameter <math>\frac{1}{\sigma_{\mathrm{w}}^{2}}</math> in the machine learning force field method.  
Description: This flag sets the precision parameter <math>s_{\mathrm{w}}</math> (see [[Machine learning force field: Theory#Bayesian linear regression|here]] for definition) for the fitting in the machine learning force field method.  
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The default value for {{TAG|ML_MODE}}=''REFIT'' works reliable in most calculations.


For the optimization of the reversed and squared noise parameter <math>\frac{1}{\sigma_{\mathrm{w}}^{2}}</math> see following section:
However, if the regularization needs to be controlled manually, like e.g. in the fitting via singular value decomposition ({{TAG|ML_MODE}}=''REFIT'' or {{TAG|ML_IALGO_LINREG}}=4), the best is to control the regularization via this parameter and keep the noise paramter <math>s_{\mathrm{v}}</math> (see {{TAG|ML_SIGV0}}) constant at 1.
{{TAG|On-the-fly machine learning force field generation using Bayesian linear regression}}.


== Related Tags and Sections ==
For the theory of this regularization parameter see [[Machine learning force field: Theory#Regression|this section]].
{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_IREG_MB}}, {{TAG|ML_FF_SIGV0_MB}}
== Related tags and articles ==
{{TAG|ML_LMLFF}}, {{TAG|ML_MODE}}, {{TAG|ML_IREG}}, {{TAG|ML_SIGV0}}, {{TAG|ML_IALGO_LINREG}}


{{sc|ML_FF_SIGW0_MB|Examples|Examples that use this tag}}
{{sc|ML_SIGW0|Examples|Examples that use this tag}}
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[[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category:VASP6]]
[[Category:INCAR tag]][[Category:Machine-learned force fields]]

Latest revision as of 16:05, 3 July 2023

ML_SIGW0 = [real]
Default: none 

Default: ML_SIGW0 = 1E-7 for ML_MODE = REFIT
= 1.0 else

Description: This flag sets the precision parameter (see here for definition) for the fitting in the machine learning force field method.


The default value for ML_MODE=REFIT works reliable in most calculations.

However, if the regularization needs to be controlled manually, like e.g. in the fitting via singular value decomposition (ML_MODE=REFIT or ML_IALGO_LINREG=4), the best is to control the regularization via this parameter and keep the noise paramter (see ML_SIGV0) constant at 1.

For the theory of this regularization parameter see this section.

Related tags and articles

ML_LMLFF, ML_MODE, ML_IREG, ML_SIGV0, ML_IALGO_LINREG

Examples that use this tag