ML ICRITERIA: Difference between revisions
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Generally it is recommended to automatically update the | 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}}. | ||
{{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}}). | {{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 | 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). | ||
== Related Tags and Sections == | == Related Tags and Sections == | ||
Revision as of 09:41, 12 June 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.
Generally it is recommended to automatically update the threshold 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 threshold ML_FF_CTIFOR is updated. To avoid too abrupt changes the average Bayesian error can be mixed with the current value of ML_FF_CTIFOR. The mixing ratio can be determined by the tag ML_FF_XMIX (default is no mixing).
Related Tags and Sections
ML_FF_LMLFF, ML_FF_CTIFOR, ML_FF_CSLOPE, ML_FF_CSIG, ML_FF_MHIS, ML_FF_XMIX