ML MHIS: Difference between revisions
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Description: This tag sets the number of estimated errors stored in memory to determine the threshold for the Bayesian error in the machine learning force field method. | Description: This tag sets the number of estimated errors stored in memory to determine the threshold for the Bayesian error in 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#Threshold for error of forces|here]]. | ||
This flag is only used if {{TAG|ML_ICRITERIA}} > 0. The ML code stores {{TAG|ML_MHIS}} Bayesian errors from previous training steps. Immediately after a training step, the Bayesian error estimate for the forces are reevaluated for the just added structure, and the maximum error of the forces is stored in the history. | This flag is only used if {{TAG|ML_ICRITERIA}} > 0. The ML code stores {{TAG|ML_MHIS}} Bayesian errors from previous training steps. Immediately after a training step, the Bayesian error estimate for the forces are reevaluated for the just added structure, and the maximum error of the forces is stored in the history. | ||
Revision as of 10:10, 2 November 2021
ML_MHIS = [integer]
Default: ML_MHIS = 10
Description: This tag sets the number of estimated errors stored in memory to determine the threshold for the Bayesian error in the machine learning force field method.
The usage of this tag in combination with the learning algorithms is described here: here.
This flag is only used if ML_ICRITERIA > 0. The ML code stores ML_MHIS Bayesian errors from previous training steps. Immediately after a training step, the Bayesian error estimate for the forces are reevaluated for the just added structure, and the maximum error of the forces is stored in the history.
Related Tags and Sections
ML_LMLFF, ML_ICRITERIA, ML_CTIFOR, ML_CSLOPE, ML_CSIG, ML_CX