ML ICRITERIA: Difference between revisions

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_LCRITERIA:

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

Examples that use this tag

@@ Line 1: / Line 1: @@
-{{TAGDEF|ML_FF_LCRITERIA|[logical]|.TRUE.}}
+{{TAGDEF|ML_ICRITERIA|[integer]|1}}
-Description: Decides whether the threshold  ({{TAG|ML_FF_CTIFOR}}) is updated in the machine learning force field methods. {{TAG|ML_FF_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.
 ----
+The following options are possible for {{TAG|ML_LCRITERIA}}:
+* {{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}} = 2: Update of criteria using gliding average of Bayesian errors.
-Generally it is recommended to automatically update the threshold {{TAG|ML_FF_CTIFOR}} during machine learning. Details on how and when the update is performed are controlled by {{TAG|ML_FF_CSLOPE}}, {{TAG|ML_FF_CSIG}} and {{TAG|ML_FF_MHIS}}.
+Generally it is recommended to automatically update the threshold {{TAG|ML_CTIFOR}} during machine learning. Details on how and when the update is performed are controlled by {{TAG|ML_CSLOPE}}, {{TAG|ML_CSIG}} and {{TAG|ML_MHIS}}.
-{{TAG|ML_FF_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  {{TAG|ML_FF_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  {{TAG|ML_FF_CSIG}}, for the update to take place. Furthermore, the slope of the stored data must be below the threshold  {{TAG|ML_FF_CSLOPE}} (we recommend to set only  {{TAG|ML_FF_CSIG}}).
+Description of {{TAG|ML_ICRITERIA}}=1 method:
+{{TAG|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  {{TAG|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  {{TAG|ML_CSIG}}, for the update to take place. Furthermore, the slope of the stored data must be below the threshold  {{TAG|ML_CSLOPE}} (we recommend to set only {{TAG|ML_CSIG}}).
-If the previous conditions are met, the threshold {{TAG|ML_FF_CTIFOR}} is updated. To avoid too abrupt changes the average Bayesian error can be mixed with the current value of  {{TAG|ML_FF_CTIFOR}}. The mixing ratio can be determined by the tag {{TAG|ML_FF_XMIX}} (default is no mixing).
+If the previous conditions are met, the threshold {{TAG|ML_CTIFOR}} is updated. To avoid too abrupt changes the average Bayesian error can be mixed with the current value of  {{TAG|ML_CTIFOR}}. The mixing ratio can be determined by the tag {{TAG|ML_XMIX}} (default is no mixing).
 == Related Tags and Sections ==
-{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_CTIFOR}},  {{TAG|ML_FF_CSLOPE}}, {{TAG|ML_FF_CSIG}}, {{TAG|ML_FF_MHIS}}, {{TAG|ML_FF_XMIX}}
+{{TAG|ML_LMLFF}}, {{TAG|ML_CTIFOR}},  {{TAG|ML_CSLOPE}}, {{TAG|ML_CSIG}}, {{TAG|ML_MHIS}}, {{TAG|ML_XMIX}}
-{{sc|ML_FF_LCRITERIA|Examples|Examples that use this tag}}
+{{sc|ML_ICRITERIA|Examples|Examples that use this tag}}
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 [[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category: Alpha]]