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 [math]\displaystyle{ s_{\mathrm{w}} }[/math] (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 [math]\displaystyle{ s_{\mathrm{v}} }[/math] (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