ML CDOUB: Difference between revisions
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Description: This tag controls the criterion for "enforced" DFT calculations within the machine learning force field method. | Description: This tag controls the criterion for "enforced" DFT calculations within the machine learning force field method. | ||
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The usage of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: | The usage of this tag in combination with the learning algorithms is described here: [[Machine learning force field calculations: Basics#Sampling of training data and local reference configurations|here]]. | ||
If at any time, the estimated force errors are {{TAG|ML_CDOUB}} times larger than the Bayesian threshold (i.e. "critically" high), a first principles calculation is performed and a new force field is immediately generated (even if the counter for sampling is below the minimum amount of sampled structures {{TAG|ML_NMDINT}}). | If at any time, the estimated force errors are {{TAG|ML_CDOUB}} times larger than the Bayesian threshold (i.e. "critically" high), a first principles calculation is performed and a new force field is immediately generated (even if the counter for sampling is below the minimum amount of sampled structures {{TAG|ML_NMDINT}}). | ||
Revision as of 10:06, 2 November 2021
ML_CDOUB = [real]
Default: ML_CDOUB = 2.0
Description: This tag controls the criterion for "enforced" DFT calculations within the machine learning force field method.
The usage of this tag in combination with the learning algorithms is described here: here.
If at any time, the estimated force errors are ML_CDOUB times larger than the Bayesian threshold (i.e. "critically" high), a first principles calculation is performed and a new force field is immediately generated (even if the counter for sampling is below the minimum amount of sampled structures ML_NMDINT).
Related Tags and Sections
ML_LMLFF, ML_MCONF_NEW, ML_CTIFOR, ML_NMDINT