ML EPS LOW: Difference between revisions
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{{TAGDEF|ML_FF_EPS_LOW|[real]|1E-10}} | {{TAGDEF|ML_FF_EPS_LOW|[real]|1E-10}} | ||
Description: Threshold for the CUR algorithm used in the sparsification of the machine learning force fields. | Description: Threshold for the CUR algorithm used in the sparsification of local reference configurations within the machine learning force fields. | ||
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This value sets the threshold for the eigenvalues that contribute to the leverage scoring used in the CUR algorithm for the rank compression ("sparsification") of the local configurations (for details see appendix E of reference {{cite|jinnouchi2:arx:2019}}). Small eigenvalues and those columns (local configurations) that are strongly connected | This value sets the threshold for the eigenvalues that contribute to the leverage scoring used in the CUR algorithm for the rank compression ("sparsification") of the local configurations (for details see appendix E of reference {{cite|jinnouchi2:arx:2019}}). Small eigenvalues and those columns (local configurations) that are strongly connected | ||
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1E-12. Unnecessary local environments can be removed in a post processing step (a single additional learning step with {{TAG|ML_FF_ISTART}}=1 using new parameters), after the on the fly learning | 1E-12. Unnecessary local environments can be removed in a post processing step (a single additional learning step with {{TAG|ML_FF_ISTART}}=1 using new parameters), after the on the fly learning | ||
has been finished. | has been finished. | ||
On the theory of the sparsification of local reference configurations see: | |||
[[Machine learning force field: Theory#Sparsification of local reference configurations]]. | |||
== Related Tags and Sections == | == Related Tags and Sections == | ||
Revision as of 18:16, 8 June 2021
ML_FF_EPS_LOW = [real]
Default: ML_FF_EPS_LOW = 1E-10
Description: Threshold for the CUR algorithm used in the sparsification of local reference configurations within the machine learning force fields.
This value sets the threshold for the eigenvalues that contribute to the leverage scoring used in the CUR algorithm for the rank compression ("sparsification") of the local configurations (for details see appendix E of reference [1]). Small eigenvalues and those columns (local configurations) that are strongly connected with these small eigenvalues are removed by the sparsification routines. The default value is fairly well balanced. However, if extensive training is performed, we recommend to reduce the threshold to 1E-12. Unnecessary local environments can be removed in a post processing step (a single additional learning step with ML_FF_ISTART=1 using new parameters), after the on the fly learning has been finished.
On the theory of the sparsification of local reference configurations see: Machine learning force field: Theory#Sparsification of local reference configurations.
Related Tags and Sections
References