ML FF ISAMPLE
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ML_FF_ISAMPLE = [integer]
Default: ML_FF_ISAMPLE = 3
Description: This tag controls the sampling in the machine learning force field method.
The following values can be used for ML_FF_ISAMPLE:
- ML_FF_ISAMPLE=1: If the estimated error is larger than the pre-determined threshold (ML_FF_CTIFOR for the Bayesian error, or ML_FF_CSF for the spilling factor), a first princiML_FF_CSF)ples calculation is performed and the structure is added to the first principles dataset. If the number of structures in the data set reaches ML_FF_MCONF_NEW the force field (FF) is updated. Collecting a set of structures allows for efficient blocking strategies in the FF update and makes the calculations significantly more efficient.
- ML_FF_ISAMPLE=2: If the estimated error is ML_FF_CDOUB times larger than one of the thresholds (ML_FF_CTIFOR or ML_FF_CSF), a first principles calculation is performed and the force field is immediately updated. If the estimated error is larger than one of the thresholds (ML_FF_CTIFOR or ML_FF_CSF), the structure is added to the first principles dataset. As before, the FF is only updated after collecting ML_FF_MCONF_NEW datasets.
- ML_FF_ISAMPLE=3: This is similar to ML_FF_ISAMPLE=2. The only difference is that as long as the upper threshold is not exceeded (e.g. ML_FF_CDOUB times ML_FF_CTIFOR), at least MD steps are preformed using the MLFF (i.e. no first principles calculation is performed). This is the default method, and avoids that many nearly identical structures are added.