ML SIGV0: Difference between revisions
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Description: This flag sets the noise parameter <math>s_{\mathrm{v}}</math> for the fitting in the machine learning force field method. | Description: This flag sets the noise parameter <math>s_{\mathrm{v}}</math> for the fitting in the machine learning force field method. | ||
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If the regularization needs to be controlled manually, like e.g. in the fitting via singular value decomposition ({{TAG|ML_IALGO_LINREG}}=4), the best is to keep this parameter constant at 1 and control the regularization via the precision parameter <math>s_{\mathrm{w}}</math> (see {{TAG|ML_SIGW0}}). | 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 keep this parameter constant at 1 and control the regularization via the precision parameter <math>s_{\mathrm{w}}</math> (see {{TAG|ML_SIGW0}}). | ||
For the theory of this regularization parameter see [[Machine learning force field: Theory#Regression|this section]]. | For the theory of this regularization parameter see [[Machine learning force field: Theory#Regression|this section]]. | ||
Revision as of 15:56, 3 July 2023
ML_SIGV0 = [real]
Default: ML_SIGV0 = 1.0
Description: This flag sets the noise parameter [math]\displaystyle{ s_{\mathrm{v}} }[/math] for the fitting in the machine learning force field method.
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 keep this parameter constant at 1 and control the regularization via the precision parameter [math]\displaystyle{ s_{\mathrm{w}} }[/math] (see ML_SIGW0).
For the theory of this regularization parameter see this section.
Related tags and sections
ML_LMLFF, ML_IREG, ML_SIGW0, ML_IALGO_LINREG