ML CTIFOR: Difference between revisions
mNo edit summary |
No edit summary |
||
| (2 intermediate revisions by one other user not shown) | |||
| Line 1: | Line 1: | ||
{{DISPLAYTITLE:ML_CTIFOR}} | {{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 | 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 | 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 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. | |||
== 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_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}} | ||
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.
Related tags and articles
ML_LMLFF, ML_ICRITERIA, ML_CALGO, ML_SCLC_CTIFOR , ML_MHIS, ML_CSIG, ML_CSLOPE, ML_CDOUB, ML_CX, ML_NMDINT, ML_MCONF_NEW