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ML_FF_LCRITERIA = [logical]

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.

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