ML SIGW0: Difference between revisions

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{{DEF|ML_SIGW0|1E-7|for {{TAG|ML_MODE}} {{=}} REFIT|1.0|else}}
{{DEF|ML_SIGW0|1E-7|for {{TAG|ML_MODE}} {{=}} REFIT|1.0|else}}


Description: This flag sets the precision parameter <math>s_{\mathrm{w}}</math> for the fitting 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.
The default value for {{TAG|ML_MODE}}=''REFIT'' works reliable in most calculations.

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