ML ICRITERIA: Difference between revisions
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Description: Decides whether ({{TAG|ML_ICRITERIA>0}}) or how the Bayesian error threshold ({{TAG|ML_CTIFOR}}) is updated in the machine learning force field methods. {{TAG|ML_CTIFOR}} determines whether a first principles calculations is performed. | Description: Decides whether ({{TAG|ML_ICRITERIA>0}}) or how the Bayesian error threshold ({{TAG|ML_CTIFOR}}) is updated in the machine learning force field methods. {{TAG|ML_CTIFOR}} determines whether a first principles calculations is performed. | ||
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The following options are possible for {{TAG| | The following options are possible for {{TAG|ML_ICRITERIA}}: | ||
* {{TAG|ML_ICRITERIA}} = 0: No update of the threshold {{TAG|ML_CTIFOR}} is done. | * {{TAG|ML_ICRITERIA}} = 0: No update of the threshold {{TAG|ML_CTIFOR}} is done. | ||
* {{TAG|ML_ICRITERIA}} = 1: Update of criteria using average of the Bayesian errors of the forces from history (see description of method below). | * {{TAG|ML_ICRITERIA}} = 1: Update of criteria using average of the Bayesian errors of the forces from history (see description of method below). | ||
Revision as of 08:42, 23 August 2021
ML_ICRITERIA = [integer]
Default: ML_ICRITERIA = 1
Description: Decides whether ([[ML_ICRITERIA>0]]) or how the Bayesian error threshold (ML_CTIFOR) is updated in the machine learning force field methods. ML_CTIFOR determines whether a first principles calculations is performed.
The following options are possible for ML_ICRITERIA:
- ML_ICRITERIA = 0: No update of the threshold ML_CTIFOR is done.
- ML_ICRITERIA = 1: Update of criteria using average of the Bayesian errors of the forces from history (see description of method below).
- ML_ICRITERIA = 2: Update of criteria using gliding average of Bayesian errors.
Generally it is recommended to automatically update the threshold ML_CTIFOR during machine learning. Details on how and when the update is performed are controlled by ML_CSLOPE, ML_CSIG and ML_MHIS.
Description of ML_ICRITERIA=1 method: ML_CTIFOR is generally set to the average of the Bayesian errors of the forces stored in a history. The number of entries in the history are controlled by ML_MHIS. To avoid that noisy data or an abrupt jump of the Bayesian error causes issues, the standard error of the history must be below the threshold ML_CSIG, for the update to take place. Furthermore, the slope of the stored data must be below the threshold ML_CSLOPE (we recommend to set only ML_CSIG).
If the previous conditions are met, the threshold ML_CTIFOR is updated. To avoid too abrupt changes the average Bayesian error can be mixed with the current value of ML_CTIFOR. The mixing ratio can be determined by the tag ML_XMIX (default is no mixing).
Related Tags and Sections
ML_LMLFF, ML_CTIFOR, ML_CSLOPE, ML_CSIG, ML_MHIS, ML_XMIX