ML RCUT2: Difference between revisions

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{{TAGDEF|ML_RCUT2|[real]|ML_RCUT1}}
{{DISPLAYTITLE:ML_RCUT2}}
{{TAGDEF|ML_RCUT2|[real]|5.0}}


Description: This flag sets the cutoff radius <math>R_\text{cut}</math> for the angular descriptor <math>\rho^{(3)}_i(r)</math> in the machine learning force field method as described in [[Machine learning force field: Theory#Descriptors|this section]].
Description: This flag sets the cutoff radius <math>R_\text{cut}</math> for the angular descriptor <math>\rho^{(3)}_i(r)</math> in the machine learning force field method. The unit of the cut-off radius is <math>\AA</math>.
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The unit of the cut-off radius is given in <math>\AA</math>.
The angular descriptor is constructed from


== Related Tags and Sections ==
<math>
{{TAG|ML_LMLFF}}, {{TAG|ML_RCUT1}}, {{TAG|ML_W1}}, {{TAG|ML_SION1}}, {{TAG|ML_SION2}}
\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>g\left(\mathbf{r}\right)</math> is an approximation of the delta function. A basis set expansion of <math>\rho^{(3)}_i(r)</math> yields the expansion coefficients <math>p_{n\nu l}^{i}</math> which are used in practice to describe the atomic environment (see [[Machine learning force field: Theory#Descriptors|this section]] for details). The tag {{TAG|ML_RCUT2}} sets the cutoff radius <math>R_\text{cut}</math> at which the cutoff function <math>f_{\mathrm{cut}}\left(r_{ij}\right)</math> decays to zero.
{{NB|mind|The cutoff radius determines how many neighbor atoms <math>N_\mathrm{a}</math> are taken into account to describe each central atom's environment. Hence, important features may be missed if the cutoff radius is set to a too small value. On the other hand, a large cutoff radius increases the computational cost as the cutoff sphere contains more neighbor atoms. A larger cutoff can also significantly degrade the learning efficiency. A good compromise is system-dependent, therefore different values should be tested to achieve satisfactory accuracy '''and''' speed.}}
 
For materials containing only H, B, C, O, N, and F (i.e. organic molecules, polymers), the learning efficiency might increase when ML_RCUT2 is set to values around 4  <math>\AA</math> (that is, less training data are required to achieve a specific accuracy), wheres for materials with very long bonds, a larger value around 6  <math>\AA</math> can improve the accuracy,
 
== Related tags and articles ==
{{TAG|ML_LMLFF}}, {{TAG|ML_RCUT1}}, {{TAG|ML_W1}}, {{TAG|ML_SION1}}, {{TAG|ML_SION2}}, {{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 13:35, 9 May 2023

ML_RCUT2 = [real]
Default: ML_RCUT2 = 5.0 

Description: This flag sets the cutoff radius for the angular descriptor in the machine learning force field method. The unit of the cut-off radius is .


The angular descriptor is constructed from

and is an approximation of the delta function. A basis set expansion of yields the expansion coefficients which are used in practice to describe the atomic environment (see this section for details). The tag ML_RCUT2 sets the cutoff radius at which the cutoff function decays to zero.

Mind: The cutoff radius determines how many neighbor atoms are taken into account to describe each central atom's environment. Hence, important features may be missed if the cutoff radius is set to a too small value. On the other hand, a large cutoff radius increases the computational cost as the cutoff sphere contains more neighbor atoms. A larger cutoff can also significantly degrade the learning efficiency. A good compromise is system-dependent, therefore different values should be tested to achieve satisfactory accuracy and speed.

For materials containing only H, B, C, O, N, and F (i.e. organic molecules, polymers), the learning efficiency might increase when ML_RCUT2 is set to values around 4 (that is, less training data are required to achieve a specific accuracy), wheres for materials with very long bonds, a larger value around 6 can improve the accuracy,

Related tags and articles

ML_LMLFF, ML_RCUT1, ML_W1, ML_SION1, ML_SION2, ML_MRB1, ML_MRB2


Examples that use this tag