ML CX: Difference between revisions
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The usage of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: Basics#Threshold for error of forces|here]]. | The usage of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: Basics#Threshold for error of forces|here]]. | ||
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}}). The number of entries in the history are controlled by {{TAG|ML_MHIS}}. | 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}}). | |||
The number of entries in the history are controlled by {{TAG|ML_MHIS}}. | |||
== Related Tags and Sections == | == Related Tags and Sections == | ||
Revision as of 11:33, 4 November 2021
ML_CX = [integer]
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) *(1.0 + ML_CX).
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