ML SION1: Difference between revisions

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{{TAGDEF|ML_SION1|[real]|0.333333}}
{{TAGDEF|ML_SION1|[real]|0.5}}


Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions for the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method (see [[Machine learning force field: Theory#Descriptors|this section]]).
Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions for the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method (see [[Machine learning force field: Theory#Descriptors|this section]]).
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The unit of {{TAG|ML_SION1}} is <math>\AA</math>.
The unit of {{TAG|ML_SION1}} is <math>\AA</math>.
{{BOX|tip|Our test calculations indicate that a ratio {{TAG|ML_SION2}} / {{TAG|ML_SION1}} {{=}} 1.5 results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for {{TAG|ML_SION2}}. Both findings together result in the above default value for {{TAG|ML_SION1}}.|Background:}}
{{BOX|note|Our test calculations indicate that {{TAG|ML_SION1}} {{=}} {{TAG|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is system-dependent, careful testing may improve machine learning results.}}


== Related Tags and Sections ==
== Related Tags and Sections ==

Revision as of 20:24, 10 October 2021

ML_SION1 = [real]
Default: ML_SION1 = 0.5 

Description: This tag specifies the width [math]\displaystyle{ \sigma_\text{atom} }[/math] of the Gaussian functions used for broadening the atomic distributions for the radial descriptor [math]\displaystyle{ \rho^{(2)}_i(r) }[/math] within the machine learning force field method (see this section).


The unit of ML_SION1 is [math]\displaystyle{ \AA }[/math].

Our test calculations indicate that ML_SION1 = ML_SION2 results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is system-dependent, careful testing may improve machine learning results.

Related Tags and Sections

ML_LMLFF, ML_SION2, ML_RCUT1, ML_RCUT2, ML_MRB1, ML_MRB2

Examples that use this tag