ML CTIFOR: Difference between revisions

Latest revision as of 14:40, 18 December 2024

Default: ML_CTIFOR	= 0.002	if ML_CALGO = 0
	= 0.02	if ML_CALGO = 1

Description: This flag sets the threshold for the error estimation in 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 error estimate of one force exceeds the threshold.

The initial threshold is set to the value provided by the tag ML_CTIFOR (units of eV/Angstrom for ML_CALGO=0 and unitless for ML_CALGO=1).

For ML_CALGO=0, the 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.

For ML_CALGO=1, only a constant threshold during the calculation is available (ML_ICRITERIA=0).

The related tag ML_SCLC_CTIFOR determines how many local reference configurations are chosen from each first principles calculations.

@@ Line 1: / Line 1: @@
-{{TAGDEF|ML_FF_CTIFOR|[real]|<math> 10^{-16}</math>}}
+{{DISPLAYTITLE:ML_CTIFOR}}
+{{DEF|ML_CTIFOR|0.002|if {{TAG|ML_CALGO}} {{=}} 0|0.02|if {{TAG|ML_CALGO}} {{=}} 1}}
-Description: This flag sets the threshold for the Bayesian error estimation on the force in the machine learning force field method.
+Description: This flag sets the threshold for the error estimation in the machine learning force field method.
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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 error estimate of one force exceeds the threshold.
-During learning, if a newly considered structure yields  a Bayesian  error in the force larger than {{TAG|ML_FF_CTIFOR}}, a first principles calculation is performed, and the corresponding structure is added to the date set of structures that are used when the force field is updated. This flag is only used if {{TAG|ML_FF_IERR}}=2 or 3. The unit of {{TAG|ML_FF_CTIFOR}} is eV/Angstrom. For more details {{TAG| ML_FF_ISAMPLE }}.
+The initial threshold is set to the value provided by the tag {{TAG|ML_CTIFOR}} (units of eV/Angstrom for {{TAG|ML_CALGO}}=0 and unitless for {{TAG|ML_CALGO}}=1).
-== Related Tags and Sections ==
+For {{TAG|ML_CALGO}}=0, the 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}}.
-{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_IERR}}, {{TAG|ML_FF_CSF}}, {{TAG| ML_FF_ISAMPLE }}
-{{sc|ML_FF_C[[Category:VASP6]]
+For {{TAG|ML_CALGO}}=1, only a constant threshold during the calculation is available ({{TAG|ML_ICRITERIA}}=0).
-TIFOR|Examples|Examples that use this tag}}
+The related tag {{TAG|ML_SCLC_CTIFOR}} determines how many local reference configurations are chosen from each first principles calculations.
+== Related tags and articles ==
+{{TAG|ML_LMLFF}}, {{TAG|ML_ICRITERIA}}, {{TAG|ML_CALGO}}, {{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}}
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-[[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category: Alpha]]
+[[Category:INCAR tag]][[Category:Machine-learned force fields]]