ML SION1: Difference between revisions

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{{TAGDEF|ML_SION1|[real]|0.333333}}
{{DISPLAYTITLE:ML_SION1}}
{{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 of the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method.
----
----
The radial descriptor is constructed from


The unit of {{TAG|ML_SION1}} is in <math>\AA</math>.
<math>
Test calculations showed that a 1.5 smaller value for the broadening of the radial descriptor compared to the angular descriptor (see {{TAG|ML_SION2}}) gives optimal results.
\rho_{i}^{(2)}\left(r\right) = \frac{1}{4\pi} \int \rho_{i}\left(r\hat{\mathbf{r}}\right) d\hat{\mathbf{r}}, \quad \text{where} \quad
The default value for {{TAG|ML_SION1}} is chosen such that {{TAG|ML_SION2}} becomes 0.5.
\rho_{i}\left(\mathbf{r}\right) = \sum\limits_{j=1}^{N_{\mathrm{a}}} f_{\mathrm{cut}}\left(r_{ij}\right) g\left(\mathbf{r}-\mathbf{r}_{ij}\right)
</math>


== Related Tags and Sections ==
and <math>g\left(\mathbf{r}\right)</math> is the following approximation of the delta function:
 
<math>
g\left(\mathbf{r}\right)=\frac{1}{\sqrt{2\sigma_{\mathrm{atom}}\pi}}\mathrm{exp}\left(-\frac{|\mathbf{r}|^{2}}{2\sigma_{\mathrm{atom}}^{2}}\right).
</math>
 
The tag {{TAG|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors|this section]] for more details).
{{BOX|tip|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 somewhat system-dependent. For instance, a smaller value for {{TAG|ML_SION1}} can increase the number of local reference configurations, and hence ultimately the quality of the MLFF. See also [[Machine learning force field calculations: Basics#Accurate force fields|here]].
}}
The unit of {{TAG|ML_SION1}} is <math>\AA</math>.
 
== Related tags and articles ==
{{TAG|ML_LMLFF}}, {{TAG|ML_SION2}}, {{TAG|ML_RCUT1}}, {{TAG|ML_RCUT2}}, {{TAG|ML_MRB1}}, {{TAG|ML_MRB2}}
{{TAG|ML_LMLFF}}, {{TAG|ML_SION2}}, {{TAG|ML_RCUT1}}, {{TAG|ML_RCUT2}}, {{TAG|ML_MRB1}}, {{TAG|ML_MRB2}}


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[[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]][[Category: Alpha]]
[[Category:INCAR tag]][[Category:Machine-learned force fields]]

Latest revision as of 13:31, 8 April 2022

ML_SION1 = [real]
Default: ML_SION1 = 0.5 

Description: This tag specifies the width of the Gaussian functions used for broadening the atomic distributions of the radial descriptor within the machine learning force field method.


The radial descriptor is constructed from

and is the following approximation of the delta function:

The tag ML_SION1 sets the width of the above Gaussian function (see this section for more details).

Tip: 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 somewhat system-dependent. For instance, a smaller value for ML_SION1 can increase the number of local reference configurations, and hence ultimately the quality of the MLFF. See also here.

The unit of ML_SION1 is .

Related tags and articles

ML_LMLFF, ML_SION2, ML_RCUT1, ML_RCUT2, ML_MRB1, ML_MRB2

Examples that use this tag