ML ICRITERIA: Difference between revisions
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{{TAGDEF|ML_FF_LCRITERIA|[logical]|.TRUE.}} | {{TAGDEF|ML_FF_LCRITERIA|[logical]|.TRUE.}} | ||
Description: Decides whether the threshold ({{TAG|ML_FF_CTIFOR}}) is updated in the machine learning force field methods. | 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. | ||
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This flag is only used if Bayesian error estimation is switched on ({{TAG|ML_FF_IERR}}=2 or 3, 3 is the default). Generally it is recommended to automatically update the criteria {{TAG|ML_FF_CTIFOR}} during machine learning. Details on | This flag is only used if Bayesian error estimation is switched on ({{TAG|ML_FF_IERR}}=2 or 3, 3 is the default). Generally it is recommended to automatically update the criteria {{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}}. | ||
{{TAG|ML_FF_CTIFOR}} is 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 | {{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}}). | ||
If the previous conditions are met, the criteria | If the previous conditions are met, the criteria {{TAG|ML_FF_CTIFOR}} is updated. To avoid too abrupt changes the average Bayesian error is mixed with the current value of {{TAG|ML_FF_CTIFOR}}. The mixing ratio can be determined by the tag {{TAG|ML_FF_XMIX}}. | ||
== Related Tags and Sections == | == Related Tags and Sections == | ||
{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_IERR}}, {{TAG|ML_FF_CTIFOR}}, {{TAG|ML_FF_CSLOPE}}, {{TAG|ML_FF_CSIG}}, {{TAG|ML_FF_MHIS}} | {{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_IERR}}, {{TAG|ML_FF_CTIFOR}}, {{TAG|ML_FF_CSLOPE}}, {{TAG|ML_FF_CSIG}}, {{TAG|ML_FF_MHIS}}, {{TAG|ML_FF_XMIX}} | ||
{{sc|ML_FF_LCRITERIA|Examples|Examples that use this tag}} | {{sc|ML_FF_LCRITERIA|Examples|Examples that use this tag}} | ||
Revision as of 10:51, 16 April 2021
ML_FF_LCRITERIA = [logical]
Default: ML_FF_LCRITERIA = .TRUE.
Description: Decides whether the threshold (ML_FF_CTIFOR) is updated in the machine learning force field methods. ML_FF_CTIFOR determines whether a first principles calculations is performed.
This flag is only used if Bayesian error estimation is switched on (ML_FF_IERR=2 or 3, 3 is the default). Generally it is recommended to automatically update the criteria ML_FF_CTIFOR during machine learning. Details on how and when the update is performed are controlled by ML_FF_CSLOPE, ML_FF_CSIG and ML_FF_MHIS.
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 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 ML_FF_CSIG, for the update to take place. Furthermore, the slope of the stored data must be below the threshold ML_FF_CSLOPE (we recommend to set only ML_FF_CSIG).
If the previous conditions are met, the criteria ML_FF_CTIFOR is updated. To avoid too abrupt changes the average Bayesian error is mixed with the current value of ML_FF_CTIFOR. The mixing ratio can be determined by the tag ML_FF_XMIX.
Related Tags and Sections
ML_FF_LMLFF, ML_FF_IERR, ML_FF_CTIFOR, ML_FF_CSLOPE, ML_FF_CSIG, ML_FF_MHIS, ML_FF_XMIX