ML EPS LOW: Difference between revisions
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{{TAGDEF|ML_EPS_LOW|[real]|1E- | {{TAGDEF|ML_EPS_LOW|[real]|1E-9}} (vasp.6.3.1 default was 1E-10, see comments below) | ||
Description: Threshold for the CUR algorithm used in the sparsification of local reference configurations within 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 | ||
with these small eigenvalues are removed by the sparsification routines. The default value is fairly well balanced | with these small eigenvalues are removed by the sparsification routines. The default value is fairly well balanced, and we do not recommend to reduce the threshold to values | ||
1E- | below 1E-9. Using smaller values than 1E-9, does not improve the MLFF if Bayesian regression is used. | ||
On the theory of the sparsification of local reference configurations see | On the theory of the sparsification of local reference configurations see | ||
Revision as of 08:56, 16 February 2022
ML_EPS_LOW = [real]
Default: ML_EPS_LOW = 1E-9 (vasp.6.3.1 default was 1E-10, see comments below)
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, and we do not recommend to reduce the threshold to values below 1E-9. Using smaller values than 1E-9, does not improve the MLFF if Bayesian regression is used.
On the theory of the sparsification of local reference configurations see here.
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