ML CSLOPE: Difference between revisions
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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. | ||
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----For details please read entry {{TAG| | ----For details please read entry {{TAG|ML_ICRITERIA}} first. The parameter {{TAG|ML_CTIFOR}} is only updated, if the absolute of the slope of the collected Bayesian errors is below {{TAG|ML_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_CSIG}} and keep {{TAG|ML_CSLOPE}} fixed to its default value. | ||
== Related Tags and Sections == | == Related Tags and Sections == | ||
{{TAG|ML_LMLFF}}, {{TAG|ML_ICRITERIA}}, {{TAG|ML_CSIG}}, {{TAG|ML_MHIS}} | {{TAG|ML_LMLFF}}, {{TAG|ML_ICRITERIA}}, {{TAG|ML_CSIG}}, {{TAG|ML_MHIS}} | ||
Revision as of 08:16, 23 August 2021
ML_CSLOPE = [real]
Default: ML_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_ICRITERIA first. The parameter ML_CTIFOR is only updated, if the absolute of the slope of the collected Bayesian errors is below ML_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_CSIG and keep ML_CSLOPE fixed to its default value.
Related Tags and Sections
ML_LMLFF, ML_ICRITERIA, ML_CSIG, ML_MHIS