ML ICRITERIA: Difference between revisions
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This flag is only used if Bayesian error estimation is switched on ({{TAG|ML_FF_IERR}}=2 or 3). Generally it is recommended to automatically update the criteria {{TAG|ML_FF_CTIFOR}} during machine learning. | This flag is only used if Bayesian error estimation is switched on ({{TAG|ML_FF_IERR}}=2 or 3). Generally it is recommended to automatically update the criteria {{TAG|ML_FF_CTIFOR}} during machine learning. Details on whether 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 the history. The number of entries in the history are controlled by {{TAG|ML_FF_MHIS}}. To avoid that noise data or an abrupt jump in the Bayesian error causes issues, the standard error of the history most be below the threshold {{TAG|ML_FF_CSIG}}. | |||
== Related Tags and Sections == | == Related Tags and Sections == | ||
Revision as of 07:05, 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.
This flag is only used if Bayesian error estimation is switched on (ML_FF_IERR=2 or 3). Generally it is recommended to automatically update the criteria ML_FF_CTIFOR during machine learning. Details on whether the update is performed are controlled by ML_FF_CSLOPE, ML_FF_CSIG and ML_FF_MHIS.
ML_FF_CTIFOR is set to the average of the Bayesian errors of the forces stored in the history. The number of entries in the history are controlled by ML_FF_MHIS. To avoid that noise data or an abrupt jump in the Bayesian error causes issues, the standard error of the history most be below the threshold ML_FF_CSIG.
Related Tags and Sections
ML_FF_LMLFF, ML_FF_IERR, ML_FF_CTIFOR, ML_FF_ISAMPLE, ML_FF_CSLOPE, ML_FF_CSIG, ML_FF_MHIS