ML CTIFOR: Difference between revisions
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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}}. | 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 is used, it is necessary to use significantly larger values around 0.02-0.06 for {{TAG|ML_CTIFOR}}. | ||
The related tag {{TAG|ML_SCLC_CTIFOR}} determines how many local reference configurations are chosen from each first principles calculations. | |||
== Related tags and articles == | == Related tags and articles == | ||
{{TAG|ML_LMLFF}}, {{TAG|ML_ICRITERIA}}, {{TAG|ML_MHIS}}, {{TAG|ML_CSIG}}, {{TAG|ML_CSLOPE}}, {{TAG|ML_CDOUB}}, {{TAG|ML_CX}}, {{TAG|ML_NMDINT}}, {{TAG|ML_MCONF_NEW}} | {{TAG|ML_LMLFF}}, {{TAG|ML_ICRITERIA}}, {{TAG|ML_SCLC_CTIFOR}} , {{TAG|ML_MHIS}}, {{TAG|ML_CSIG}}, {{TAG|ML_CSLOPE}}, {{TAG|ML_CDOUB}}, {{TAG|ML_CX}}, {{TAG|ML_NMDINT}}, {{TAG|ML_MCONF_NEW}} | ||
{{sc|ML_CTIFOR|Examples|Examples that use this tag}} | {{sc|ML_CTIFOR|Examples|Examples that use this tag}} | ||
Latest revision as of 06:46, 13 October 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 is used, it is necessary to use significantly larger values around 0.02-0.06 for ML_CTIFOR.
The related tag ML_SCLC_CTIFOR determines how many local reference configurations are chosen from each first principles calculations.
Related tags and articles
ML_LMLFF, ML_ICRITERIA, ML_SCLC_CTIFOR , ML_MHIS, ML_CSIG, ML_CSLOPE, ML_CDOUB, ML_CX, ML_NMDINT, ML_MCONF_NEW