ML SION2: Difference between revisions
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{{TAGDEF|ML_SION2|[real]| | {{TAGDEF|ML_SION2|[real]|{{TAG|ML_SION1}}}} | ||
Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions of the angular descriptor <math>\rho^{(3)}_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 of the angular descriptor <math>\rho^{(3)}_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_SION2}} is <math>\AA</math>. | The unit of {{TAG|ML_SION2}} is <math>\AA</math>. | ||
{{BOX| | {{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 20:26, 10 October 2021
ML_SION2 = [real]
Default: ML_SION2 = ML_SION1
Description: This tag specifies the width [math]\displaystyle{ \sigma_\text{atom} }[/math] of the Gaussian functions used for broadening the atomic distributions of the angular descriptor [math]\displaystyle{ \rho^{(3)}_i(r) }[/math] within the machine learning force field method (see this section).
The unit of ML_SION2 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_SION1, ML_RCUT1, ML_RCUT2, ML_MRB1, ML_MRB2