ML CTIFOR: Difference between revisions
(Added typical values that ML_CTIFOR will attain.) |
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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 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 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}}. Typically, after extensive training, attainable values for ML_CTIFOR are 0.02 around 300-500 K, and 0.06 around 1000-2000 K, so temperature but also system dependent. The initial default 0.002 is only, if {{TAG|ML_CTIFOR}} is automatically updated ({{TAG|ML_ICRITERIA}} = 1 or 2). If {{TAG|ML_ICRITERIA}} = 0, use significantly larger values around 0.02-0.06. | 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}}. Typically, after extensive training, attainable values for ML_CTIFOR are 0.02 around 300-500 K, and 0.06 around 1000-2000 K, so temperature but also system dependent. The initial default 0.002 is only sensible, if {{TAG|ML_CTIFOR}} is automatically updated ({{TAG|ML_ICRITERIA}} = 1 or 2). If {{TAG|ML_ICRITERIA}} = 0, use significantly larger values around 0.02-0.06. | ||
== Related tags and articles == | == Related tags and articles == | ||
Revision as of 08:55, 2 April 2023
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. Typically, after extensive training, attainable values for ML_CTIFOR are 0.02 around 300-500 K, and 0.06 around 1000-2000 K, so temperature but also system dependent. The initial default 0.002 is only sensible, if ML_CTIFOR is automatically updated (ML_ICRITERIA = 1 or 2). If ML_ICRITERIA = 0, use significantly larger values around 0.02-0.06.
Related tags and articles
ML_LMLFF, ML_ICRITERIA, ML_MHIS, ML_CSIG, ML_CSLOPE, ML_CDOUB, ML_CX, ML_NMDINT, ML_MCONF_NEW