ML CX: Difference between revisions

Revision as of 11:51, 23 November 2021

ML_CX = [real]
Default: ML_CX = 0.0

Description: The parameter determines how the threshold (ML_CTIFOR) is updated within the machine learning force field methods.

The usage of this tag in combination with the learning algorithms is described here: here.

If ML_ICRITERIA>0, ML_CTIFOR is set to the average of the Bayesian errors of the forces stored in history (see ML_ICRITERIA), specifically,

ML_CTIFOR = (average of the stored Bayesian errors in the history) *(1.0 + ML_CX).

Obviously setting ML_CX to a positive value will result in fewer first principles calculations and fewer updates of the MLFF, whereas negative values result in more frequent first principles calculations (as well as updates of the MLFF). Typical values of ML_CX are between -0.3 and 0. For training runs using heating, the default usually results in very well balanced machine learned force fields. When the training is performed at a fixed temperature, it is often desirable to decrease to ML_CX=-0.1, in order to increase the number of first principle calculations and thus the size of the training set (the default can result in too few training data).

The number of entries in the history are controlled by ML_MHIS.

Related Tags and Sections

ML_LMLFF, ML_ICRITERIA, ML_CTIFOR, ML_MHIS, ML_CSIG, ML_CSLOPE

Examples that use this tag

@@ Line 7: / Line 7: @@
 If {{TAG|ML_ICRITERIA}}>0, {{TAG|ML_CTIFOR}} is set to the average of the Bayesian errors of the forces stored in history (see {{TAG|ML_ICRITERIA}}), specifically,
-{{TAG|ML_CTIFOR}} = (average of the stored Bayesian errors) *(1.0 + {{TAG|ML_CX}}).
+{{TAG|ML_CTIFOR}} = (average of the stored Bayesian errors in the history) *(1.0 + {{TAG|ML_CX}}).
-Obviously setting  {{TAG|ML_CX}} to a positive value will result in fewer first principles calculations and fewer updates of the MLFF, whereas negative values result in more frequent first principles calculations (as well as updates of the MLFF). Typical values of  {{TAG|ML_CX}} are between -0.3 and 0. For heating run the default usually results in very consistent and well balanced machine learned force fields. When the training is performed at a fixed temperature, it is often desirable to decrease to ML_CX=-0.1, in order to increase the number of first principle calculations and thus the size of the training set (the default can result in too few training data).
+Obviously setting  {{TAG|ML_CX}} to a positive value will result in fewer first principles calculations and fewer updates of the MLFF, whereas negative values result in more frequent first principles calculations (as well as updates of the MLFF). Typical values of  {{TAG|ML_CX}} are between -0.3 and 0. For training runs using heating, the default usually results in very well balanced machine learned force fields. When the training is performed at a fixed temperature, it is often desirable to decrease to {{TAG|ML_CX}}=-0.1, in order to increase the number of first principle calculations and thus the size of the training set (the default can result in too few training data).
 The number of entries in the history are controlled by  {{TAG|ML_MHIS}}.
-== Related Tags and Sections ==
+== Related Tags and Sections==
 {{TAG|ML_LMLFF}}, {{TAG|ML_ICRITERIA}}, {{TAG|ML_CTIFOR}}, {{TAG|ML_MHIS}}, {{TAG|ML_CSIG}}, {{TAG|ML_CSLOPE}}