ML SION2: Difference between revisions

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{{TAGDEF|ML_FF_SION2_MB|[real]}}
{{DISPLAYTITLE:ML_SION2}}
{{DEF|ML_FF_SION2_MB|none|if {{TAG|ML_FF_IBROAD2_MB}}{{=}}1|0.5|otherwise}}
{{TAGDEF|ML_SION2|[real]|{{TAG|ML_SION1}}}}


Description: This tag specifies the width of the Gaussian functions used for broadening the atomic distributions of the angular descriptor within the machine learning force field method.
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.
----
----
The unit of {{TAG|ML_FF_SION2_MB}} is in <math>\AA</math>.
The angular descriptor is constructed from
Test calculations showed that a 1.5 smaller value for the broadening of the radial descriptor compared to the angular descriptor (see {{TAG|ML_FF_SION2_MB}}) gives optimal results.
This tag is not used if {{TAG|ML_FF_IBROAD1_MB}}=1 (which is not the default).


<math>
\rho_{i}^{(3)}\left(r,s,\theta\right) = \iint d\hat{\mathbf{r}} d\hat{\mathbf{s}}  \delta\left(\hat{\mathbf{r}}\cdot\hat{\mathbf{s}} - \mathrm{cos}\theta\right) \sum\limits_{j=1}^{N_{a}} \sum\limits_{k \ne j}^{N_{a}} \rho_{ik} \left(r\hat{\mathbf{r}}\right) \rho_{ij} \left(s\hat{\mathbf{s}}\right), \quad \text{where} \quad
\rho_{ij}\left(\mathbf{r}\right) = 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:
{{TAG|ML_FF_LMLFF}}, {{TAG|ML_FF_SION1_MB}}, {{TAG|ML_FF_RCUT1_MB}}, {{TAG|ML_FF_RCUT2_MB}}, {{TAG|ML_FF_MRB1_MB}}, {{TAG|ML_FF_MRB2_MB}}


{{sc|ML_FF_SION2_MB|Examples|Examples that use this tag}}
<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_SION2}} 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 system-dependent, careful testing may improve machine learning results.}}
The unit of {{TAG|ML_SION2}} is <math>\AA</math>.
 
== Related tags and articles ==
{{TAG|ML_LMLFF}}, {{TAG|ML_SION1}}, {{TAG|ML_RCUT1}}, {{TAG|ML_RCUT2}}, {{TAG|ML_MRB1}}, {{TAG|ML_MRB2}}
 
{{sc|ML_SION2|Examples|Examples that use this tag}}
----
----


[[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_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.


The angular descriptor is constructed from

[math]\displaystyle{ \rho_{i}^{(3)}\left(r,s,\theta\right) = \iint d\hat{\mathbf{r}} d\hat{\mathbf{s}} \delta\left(\hat{\mathbf{r}}\cdot\hat{\mathbf{s}} - \mathrm{cos}\theta\right) \sum\limits_{j=1}^{N_{a}} \sum\limits_{k \ne j}^{N_{a}} \rho_{ik} \left(r\hat{\mathbf{r}}\right) \rho_{ij} \left(s\hat{\mathbf{s}}\right), \quad \text{where} \quad \rho_{ij}\left(\mathbf{r}\right) = f_{\mathrm{cut}}\left(r_{ij}\right) g\left(\mathbf{r}-\mathbf{r}_{ij}\right) }[/math]

and [math]\displaystyle{ g\left(\mathbf{r}\right) }[/math] is the following approximation of the delta function:

[math]\displaystyle{ 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 ML_SION2 sets the width [math]\displaystyle{ \sigma_\text{atom} }[/math] 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 system-dependent, careful testing may improve machine learning results.

The unit of ML_SION2 is [math]\displaystyle{ \AA }[/math].

Related tags and articles

ML_LMLFF, ML_SION1, ML_RCUT1, ML_RCUT2, ML_MRB1, ML_MRB2

Examples that use this tag