ML SIGW0: Difference between revisions
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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. | ||
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The default value for {{TAG|ML_MODE}}=''REFIT'' works | 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. | ||
Latest 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