ML CX: Difference between revisions
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{{TAG|ML_CTIFOR}} = (average of the stored Bayesian errors) *(1.0 + {{TAG|ML_CX}}). | {{TAG|ML_CTIFOR}} = (average of the stored Bayesian errors) *(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. | |||
The number of entries in the history are controlled by {{TAG|ML_MHIS}}. | The number of entries in the history are controlled by {{TAG|ML_MHIS}}. | ||
Revision as of 11:36, 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).
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.
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