ML CTIFOR: Difference between revisions
mNo edit summary |
mNo edit summary |
||
| Line 4: | Line 4: | ||
Description: This flag sets the threshold for the Bayesian error estimation on the force within the machine learning force field method. | Description: This flag sets the threshold for the Bayesian error estimation on the force within the machine learning force field method. | ||
---- | ---- | ||
The use of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: Basics#Sampling of training data and local reference configurations|here]]. | The use of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: Basics#Sampling of training data and local reference configurations|here]]. Generally, first principles calculations are only performed if the Bayesian error estimate of one force exceeds the threshold. | ||
The initial threshold is set to the value provided by {{TAG|ML_CTIFOR}} (the unit is eV/Angstrom). This threshold can be updated dynamically during ML. The details of the update are controlled by {{TAG|ML_ICRITERIA}}. | The initial threshold is set to the value provided by the tag {{TAG|ML_CTIFOR}} (the unit is eV/Angstrom). This threshold can be updated dynamically during ML. The details of the update are controlled by {{TAG|ML_ICRITERIA}}. | ||
== Related tags and articles == | == Related tags and articles == | ||
Revision as of 06:41, 18 September 2022
ML_CTIFOR = [real]
Default: ML_CTIFOR =
Description: This flag sets the threshold for the Bayesian error estimation on the force within the machine learning force field method.
The use of this tag in combination with the learning algorithms is described here: here. Generally, first principles calculations are only performed if the Bayesian error estimate of one force exceeds the threshold.
The initial threshold is set to the value provided by the tag ML_CTIFOR (the unit is eV/Angstrom). This threshold can be updated dynamically during ML. The details of the update are controlled by ML_ICRITERIA.
Related tags and articles
ML_LMLFF, ML_ICRITERIA, ML_MHIS, ML_CSIG, ML_CSLOPE, ML_CDOUB, ML_CX, ML_NMDINT, ML_MCONF_NEW