ML SCLC CTIFOR: Difference between revisions
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Description: Sets fraction by which the Bayesian threshold for the maximum forces is lowered in the selection of local reference calculations. | Description: Sets fraction by which the Bayesian threshold for the maximum forces is lowered in the selection of local reference calculations. | ||
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{{TAG|ML_CTIFOR}} determines whether a first principles calculation is performed when training an MLFF. | {{TAG|ML_CTIFOR}} determines whether a first-principles calculation is performed when training an MLFF. | ||
Whenever a first-principles calculation is performed, | Whenever a first-principles calculation is performed, additional functions are added to the sparse representation of the kernel (local-reference configurations). {{TAG | ML_SCLC_CTIFOR}} determines how many local-reference configurations are added to the sparse representation of the kernel. | ||
Specifically, the local environment of those atoms with a Bayesian error larger than {{TAG | ML_SCLC_CTIFOR}} * {{TAG|ML_CTIFOR}} are | Specifically, the local environment of those atoms with a Bayesian error larger than {{TAG | ML_SCLC_CTIFOR}} * {{TAG|ML_CTIFOR}} are | ||
added as candidates for the sparse representational of the kernel. Note that changing {{TAG | ML_SCLC_CTIFOR}} does not change the decision whether a first-principles calculation is carried out or not, since this decision | added as candidates for the sparse representational of the kernel. Note that changing {{TAG | ML_SCLC_CTIFOR}} does not change the decision of whether a first-principles calculation is carried out or not, since this decision is entirely based on {{TAG|ML_CTIFOR}}. | ||
The default value of 0.6 is often a reasonably good compromise. If the value is decreased, obviously more functions are used for the sparse representation of the kernel. This always improves | The default value of 0.6 is often a reasonably good compromise. If the value is decreased, obviously more functions are used for the sparse representation of the kernel. This always improves the initial learning efficiency but might slow the force-field calculations. | ||
So {{TAG | ML_SCLC_CTIFOR}} compromises either learning efficiency or the speed of the evaluation of the MLFF. | So {{TAG | ML_SCLC_CTIFOR}} compromises either learning efficiency or the speed of the evaluation of the MLFF. | ||
For polymers and liquids, we found that decreasing {{TAG | ML_SCLC_CTIFOR}} to values around 0.4 (or even smaller) can significantly improve learning efficiency. | For polymers and liquids, we found that decreasing {{TAG | ML_SCLC_CTIFOR}} to values around 0.4 (or even smaller) can significantly improve learning efficiency. | ||
Revision as of 09:30, 13 October 2023
ML_SCLC_CTIFOR = [real]
Default: ML_SCLC_CTIFOR = 0.6
Description: Sets fraction by which the Bayesian threshold for the maximum forces is lowered in the selection of local reference calculations.
ML_CTIFOR determines whether a first-principles calculation is performed when training an MLFF. Whenever a first-principles calculation is performed, additional functions are added to the sparse representation of the kernel (local-reference configurations). ML_SCLC_CTIFOR determines how many local-reference configurations are added to the sparse representation of the kernel. Specifically, the local environment of those atoms with a Bayesian error larger than ML_SCLC_CTIFOR * ML_CTIFOR are added as candidates for the sparse representational of the kernel. Note that changing ML_SCLC_CTIFOR does not change the decision of whether a first-principles calculation is carried out or not, since this decision is entirely based on ML_CTIFOR.
The default value of 0.6 is often a reasonably good compromise. If the value is decreased, obviously more functions are used for the sparse representation of the kernel. This always improves the initial learning efficiency but might slow the force-field calculations. So ML_SCLC_CTIFOR compromises either learning efficiency or the speed of the evaluation of the MLFF. For polymers and liquids, we found that decreasing ML_SCLC_CTIFOR to values around 0.4 (or even smaller) can significantly improve learning efficiency.
Related tags and articles
ML_LMLFF, ML_CTIFOR, ML_CX, ML_EPS_LOW