ML CSLOPE: Difference between revisions
No edit summary |
mNo edit summary |
||
| Line 3: | Line 3: | ||
Description: Parameter used in the automatic determination of threshold for Bayesian error estimation in the machine learning force field method. | Description: Parameter used in the automatic determination of threshold for Bayesian error estimation in the machine learning force field method. | ||
---- | ---- | ||
----For details please read entry {{TAG|ML_FF_LCRITERIA}} first. The parameter {{TAG|ML_FF_CTIFOR}} is only updated, if the absolute of the slope of the collected Bayesian errors is below {{TAG|ML_FF_CSLOPE}} times the mean of the collected Bayesian errors. In practice, the slope and the standard errors are correlated: typically the standard error is at least twice the slope. We recommend to vary | ----For details please read entry {{TAG|ML_FF_LCRITERIA}} first. The parameter {{TAG|ML_FF_CTIFOR}} is only updated, if the absolute of the slope of the collected Bayesian errors is below {{TAG|ML_FF_CSLOPE}} times the mean of the collected Bayesian errors. In practice, the slope and the standard errors are correlated: typically the standard error is at least twice the slope. We recommend to vary only {{TAG|ML_FF_CSIG}} and keep {{TAG|ML_FF_CSLOPE}} fixed to its default value. | ||
== Related Tags and Sections == | == Related Tags and Sections == | ||
{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_IERR}}, {{TAG|ML_FF_ISAMPLE}}, {{TAG|ML_FF_LCRITERIA}}, {{TAG|ML_FF_CSIG}}, {{TAG|ML_FF_MHIS}} | {{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_IERR}}, {{TAG|ML_FF_ISAMPLE}}, {{TAG|ML_FF_LCRITERIA}}, {{TAG|ML_FF_CSIG}}, {{TAG|ML_FF_MHIS}} | ||
Revision as of 08:17, 16 April 2021
ML_FF_CSLOPE = [real]
Default: ML_FF_CSLOPE =
Description: Parameter used in the automatic determination of threshold for Bayesian error estimation in the machine learning force field method.
For details please read entry ML_FF_LCRITERIA first. The parameter ML_FF_CTIFOR is only updated, if the absolute of the slope of the collected Bayesian errors is below ML_FF_CSLOPE times the mean of the collected Bayesian errors. In practice, the slope and the standard errors are correlated: typically the standard error is at least twice the slope. We recommend to vary only ML_FF_CSIG and keep ML_FF_CSLOPE fixed to its default value.
Related Tags and Sections
ML_FF_LMLFF, ML_FF_IERR, ML_FF_ISAMPLE, ML_FF_LCRITERIA, ML_FF_CSIG, ML_FF_MHIS