ML SION1: Difference between revisions
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{{DISPLAYTITLE:ML_SION1}} | |||
{{TAGDEF|ML_SION1|[real]|0.5}} | {{TAGDEF|ML_SION1|[real]|0.5}} | ||
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The unit of {{TAG|ML_SION1}} is <math>\AA</math>. | The unit of {{TAG|ML_SION1}} is <math>\AA</math>. | ||
== Related | == 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:INCAR tag]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]] |
Revision as of 07:42, 7 April 2022
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 of the radial descriptor [math]\displaystyle{ \rho^{(2)}_i(r) }[/math] within the machine learning force field method.
The radial descriptor is constructed from
[math]\displaystyle{ \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 \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]
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_SION1 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 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 [math]\displaystyle{ \AA }[/math].
Related tags and articles
ML_LMLFF, ML_SION2, ML_RCUT1, ML_RCUT2, ML_MRB1, ML_MRB2