ML SIGW0: Difference between revisions

From VASP Wiki
No edit summary
(small typo corrected)
Line 5: Line 5:
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.  
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.  
----
----
The default value for {{TAG|ML_MODE}}=''REFIT'' works reliable in most calculations.
The default value for {{TAG|ML_MODE}}=''REFIT'' works reliably in most calculations.


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.
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.

Revision as of 17:13, 7 August 2024

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 reliably 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