ML EPS LOW: Difference between revisions
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{{TAGDEF| | {{DISPLAYTITLE:ML_EPS_LOW}} | ||
{{TAGDEF|ML_EPS_LOW|[real]}} | |||
{{DEF|ML_EPS_LOW|1E-11|for {{TAG|ML_MODE}} {{=}} SELECT, REFIT|1E-9|else (vasp.6.3.0 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 {{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, and we do not recommend to increase the threshold to values | |||
larger than 1E-7. Also using smaller values than 1E-9 does not improve the MLFF if Bayesian regression is used | |||
(but it can be benficial for SVD). | |||
The description how to choose {{TAG|ML_EPS_LOW}} for accurate force fields is given [[Best practices for machine-learned force fields#Accurate force fields|here]]. | |||
On the theory of the sparsification of local reference configurations see | |||
[[Machine learning force field: Theory#Sparsification of local reference configurations|here]]. | |||
== Related tags and articles == | |||
{{TAG|ML_LMLFF}}, {{TAG|ML_MB}}, {{TAG|ML_EPS_REG}}, {{TAG|ML_IALGO_LINREG}} | |||
{{sc|ML_EPS_LOW|Examples|Examples that use this tag}} | |||
== | == References == | ||
<references/> | |||
<noinclude> | |||
---- | ---- | ||
[[Category:INCAR]][[Category:Machine | [[Category:INCAR tag]][[Category:Machine-learned force fields]] | ||
Latest revision as of 11:42, 15 May 2023
ML_EPS_LOW = [real]
| Default: ML_EPS_LOW | = 1E-11 | for ML_MODE = SELECT, REFIT |
| = 1E-9 | else (vasp.6.3.0 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 increase the threshold to values larger than 1E-7. Also using smaller values than 1E-9 does not improve the MLFF if Bayesian regression is used (but it can be benficial for SVD).
The description how to choose ML_EPS_LOW for accurate force fields is given here.
On the theory of the sparsification of local reference configurations see here.
Related tags and articles
ML_LMLFF, ML_MB, ML_EPS_REG, ML_IALGO_LINREG
References