ML W1: Difference between revisions

Latest revision as of 13:31, 8 April 2022

ML_W1 = [real]
Default: ML_W1 = 0.1

Description: This tag defines the weight $\beta$ for the radial (and angular) descriptor within the machine learning force field method (see this section).

The weight for the angular descriptor $W_{2}$ is internally computed from the weight of the radial descriptor $W_{1}$ as:

$W_{2}=1.0-W_{1}.$

The value for ML_W1 must be chosen in the interval $[0, 1]$ .

By default, the angular and radial descriptors are both used although the latter is weighed less. In principle a weight of 0 for one of them is selectable which allows the code to internally skip the respective computation. However, it is generally recommended to use both descriptors to achieve satisfying training results.

@@ Line 1: / Line 1: @@
-{{TAGDEF|ML_W1|[real]|0.5}}
+{{DISPLAYTITLE:ML_W1}}
+{{TAGDEF|ML_W1|[real]|0.1}}
-Description: This tag defines the weight for the radial (and angular) descriptor within the machine learning force field method.
+Description: This tag defines the weight <math>\beta</math> for the radial (and angular) descriptor within the machine learning force field method (see [[Machine learning force field: Theory#Potential energy fitting|this section]]).
 ----
-The weight for the angular descriptor <math>W_{2}</math> is internally derived from the weight of the radial descriptor <math>W_{1}</math> as:
+The weight for the angular descriptor <math>W_{2}</math> is internally computed from the weight of the radial descriptor <math>W_{1}</math> as:
 <math>W_{2}=1.0-W_{1}.</math>
-By default this descriptor is used together with the angular descriptor with equal weights. If the weight of either becomes 0 then the internal routines for that descriptor are mostly skipped.
+The value for {{TAG|ML_W1}} must be chosen in the interval <math>[0, 1]</math>.
-== Related Tags and Sections ==
+By default, the angular and radial descriptors are both used although the latter is weighed less. In principle a weight of 0 for one of them is selectable which allows the code to internally skip the respective computation. However, it is generally recommended to use both descriptors to achieve satisfying training results.
+== Related tags and articles ==
 {{TAG|ML_LMLFF}}, {{TAG|ML_RCUT1}}, {{TAG|ML_RCUT2}}, {{TAG|ML_SION1}}, {{TAG|ML_SION2}}
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-[[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category: Alpha]]
+[[Category:INCAR tag]][[Category:Machine-learned force fields]]