ML CTIFOR - Revision history

Karsai at 14:40, 18 December 2024

2024-12-18T14:40:41Z

← Older revision		Revision as of 14:40, 18 December 2024
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	{{DISPLAYTITLE:ML_CTIFOR}}		{{DISPLAYTITLE:ML_CTIFOR}}
	{{~~TAGDEF~~\|ML_CTIFOR\|~~[real]~~\|~~<math>~~ 0.~~002</math>~~}}		{{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 within~~ the machine learning force field method.		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: [[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 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 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 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).

			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}}.

			For {{TAG\|ML_CALGO}}=1, only a constant threshold during the calculation is available ({{TAG\|ML_ICRITERIA}}=0).

	The related tag {{TAG\|ML_SCLC_CTIFOR}} determines how many local reference configurations are chosen from each first principles calculations.		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_SCLC_CTIFOR}} , {{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_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}}		{{sc\|ML_CTIFOR\|Examples\|Examples that use this tag}}

Kresse: Reference to ML_SCLC_CTIFOR added

2023-10-13T06:46:24Z

Reference to ML_SCLC_CTIFOR added

← Older revision		Revision as of 06:46, 13 October 2023
Line 7:		Line 7:

	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 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}}

Kresse at 06:40, 13 October 2023

2023-10-13T06:40:46Z

← Older revision		Revision as of 06:40, 13 October 2023
Line 6:		Line 6:
	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 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.		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}}.

	== Related tags and articles ==		== Related tags and articles ==

Kresse at 08:55, 2 April 2023

2023-04-02T08:55:59Z

← Older revision		Revision as of 08:55, 2 April 2023
Line 6:		Line 6:
	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 ==

Kresse: Added typical values that ML_CTIFOR will attain.

2023-04-02T08:55:01Z

Added typical values that ML_CTIFOR will attain.

← Older revision		Revision as of 08:55, 2 April 2023
Line 6:		Line 6:
	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, 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 ==

Kresse at 06:41, 18 September 2022

2022-09-18T06:41:59Z

← Older revision		Revision as of 06:41, 18 September 2022
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 ==

Kresse at 15:21, 17 September 2022

2022-09-17T15:21:41Z

← Older revision		Revision as of 15:21, 17 September 2022
Line 6:		Line 6:
	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]].

	The initial threshold is set to the value provided by {{TAG\|ML_CTIFOR}} (the unit is eV/Angstrom). The ~~behaviour how~~ the ~~threshold is further~~ controlled ~~is given~~ by {{TAG\|ML_ICRITERIA}}.		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}}.

	== Related tags and articles ==		== Related tags and articles ==

Karsai at 13:21, 8 April 2022

2022-04-08T13:21:37Z

← Older revision		Revision as of 13:21, 8 April 2022
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	----		----

	[[Category:INCAR tag]][[Category:Machine ~~Learning]][[Category:Machine Learned Force Fields]][[Category:VASP6~~]]		[[Category:INCAR tag]][[Category:Machine-learned force fields]]

Karsai at 07:21, 7 April 2022

2022-04-07T07:21:20Z

← Older revision		Revision as of 07:21, 7 April 2022
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			{{DISPLAYTITLE:ML_CTIFOR}}
	{{TAGDEF\|ML_CTIFOR\|[real]\|<math> 0.002</math>}}		{{TAGDEF\|ML_CTIFOR\|[real]\|<math> 0.002</math>}}

Line 7:		Line 8:
	The initial threshold is set to the value provided by {{TAG\|ML_CTIFOR}} (the unit is eV/Angstrom). The behaviour how the threshold is further controlled is given by {{TAG\|ML_ICRITERIA}}.		The initial threshold is set to the value provided by {{TAG\|ML_CTIFOR}} (the unit is eV/Angstrom). The behaviour how the threshold is further controlled is given by {{TAG\|ML_ICRITERIA}}.

	== Related ~~Tags~~ and ~~Sections~~ ==		== 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_MHIS}}, {{TAG\|ML_CSIG}}, {{TAG\|ML_CSLOPE}}, {{TAG\|ML_CDOUB}}, {{TAG\|ML_CX}}, {{TAG\|ML_NMDINT}}, {{TAG\|ML_MCONF_NEW}}

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	----		----

	[[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category:VASP6~~]][[Category: Alpha~~]]		[[Category:INCAR tag]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category:VASP6]]

Kresse at 17:37, 23 March 2022

2022-03-23T17:37:26Z

← Older revision		Revision as of 17:37, 23 March 2022
Line 3:		Line 3:
	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 ~~usage~~ 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]].

	The initial threshold is set to the value provided by {{TAG\|ML_CTIFOR}} (the unit is eV/Angstrom). The behaviour how the threshold is further controlled is given by {{TAG\|ML_ICRITERIA}}.		The initial threshold is set to the value provided by {{TAG\|ML_CTIFOR}} (the unit is eV/Angstrom). The behaviour how the threshold is further controlled is given by {{TAG\|ML_ICRITERIA}}.

← Older revision		Revision as of 14:40, 18 December 2024
Line 1:		Line 1:
	{{DISPLAYTITLE:ML_CTIFOR}}		{{DISPLAYTITLE:ML_CTIFOR}}
	{{~~TAGDEF~~\|ML_CTIFOR\|~~[real]~~\|~~<math>~~ 0.~~002</math>~~}}		{{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 within~~ the machine learning force field method.		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: [[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 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 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 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).

			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}}.

			For {{TAG\|ML_CALGO}}=1, only a constant threshold during the calculation is available ({{TAG\|ML_ICRITERIA}}=0).

	The related tag {{TAG\|ML_SCLC_CTIFOR}} determines how many local reference configurations are chosen from each first principles calculations.		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_SCLC_CTIFOR}} , {{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_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}}		{{sc\|ML_CTIFOR\|Examples\|Examples that use this tag}}

← Older revision		Revision as of 06:46, 13 October 2023
Line 7:		Line 7:

	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 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}}