ML SIGW0: Difference between revisions
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The default value works empirically very good in most calculations | The default value works empirically very good in most calculations | ||
However, 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 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. | ||
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 15:54, 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 for the fitting in the machine learning force field method.
The default value works empirically very good 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