ML MCONF NEW: Difference between revisions
No edit summary |
No edit summary |
||
| (19 intermediate revisions by 3 users not shown) | |||
| Line 1: | Line 1: | ||
{{DISPLAYTITLE:ML_MCONF_NEW}} | |||
{{TAGDEF|ML_MCONF_NEW|[integer]|5}} | {{TAGDEF|ML_MCONF_NEW|[integer]|5}} | ||
Description: This tag sets the number of configurations that are stored temporarily as candidates for the training data in the machine learning force field method. | Description: This tag sets the number of configurations that are stored temporarily as candidates for the training data in the machine learning force field method. | ||
---- | ---- | ||
{{NB|warning|This value is | {{NB|warning|This value is close to optimal for on-the-fly learning, and should usually not be changed. }} | ||
== Related | 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]]. | ||
If force fields are reparameterized ({{TAG|ML_MODE|select}}), calculations are usually more efficient if this parameter is increased to values around 10-16 and setting {{TAG|ML_CDOUB|4}}. This is particularly relevant if the ML_AB file is large. | |||
== Related tags and articles == | |||
{{sc|ML_MCONF_NEW|Examples|Examples that use this tag}} | {{sc|ML_MCONF_NEW|Examples|Examples that use this tag}} | ||
---- | ---- | ||
{{TAG|ML_LMLFF}}, {{TAG|ML_MCONF}} | {{TAG|ML_LMLFF}}, {{TAG|ML_MCONF}}, {{TAG|ML_CTIFOR}}, {{TAG|ML_CDOUB}} | ||
[[Category:INCAR]][[Category:Machine | [[Category:INCAR tag]][[Category:Machine-learned force fields]] | ||
Latest revision as of 07:56, 24 October 2025
ML_MCONF_NEW = [integer]
Default: ML_MCONF_NEW = 5
Description: This tag sets the number of configurations that are stored temporarily as candidates for the training data in the machine learning force field method.
| Warning: This value is close to optimal for on-the-fly learning, and should usually not be changed. |
The use of this tag in combination with the learning algorithms is described here: here.
If force fields are reparameterized (ML_MODE = select), calculations are usually more efficient if this parameter is increased to values around 10-16 and setting ML_CDOUB = 4. This is particularly relevant if the ML_AB file is large.