ML RCUT1: Difference between revisions

From VASP Wiki
No edit summary
 
(6 intermediate revisions by 3 users not shown)
Line 1: Line 1:
{{TAGDEF|ML_RCUT1|[real]|5.0}}
{{DISPLAYTITLE:ML_RCUT1}}
{{TAGDEF|ML_RCUT1|[real]|8.0}}


Description: This flag sets the cutoff radius <math>R_\text{cut}</math> for the radial descriptor <math>\rho^{(2)}_i(r)</math> in the machine learning force field method.
Description: Sets the cutoff radius <math>R_\text{cut}</math> for the radial descriptor <math>\rho^{(2)}_i(r)</math> in <math>\AA</math>.
----
----
The radial descriptor is constructed from
The radial descriptor for machine-learned force fields is constructed from


<math>
<math>
Line 10: Line 11:
</math>
</math>


and <math>g\left(\mathbf{r}\right)</math> is an approximation of the delta function. A basis set expansion of <math>\rho^{(2)}_i(r)</math> yields the expansion coefficients <math>c_{n00}^{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_RCUT1}} 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.
and <math>g\left(\mathbf{r}\right)</math> is an approximation of the delta function. A basis set expansion of <math>\rho^{(2)}_i(r)</math> yields the expansion coefficients <math>c_{n00}^{i}</math>, which are used in practice to describe the atomic environment; refer to the [[Machine learning force field: Theory#Descriptors|theory of machine-learned force fields]] for details. The tag {{TAG|ML_RCUT1}} 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 of the descriptor as the cutoff sphere contains more neighbor atoms. A good compromise is always system-dependent, therefore different values should be tested to achieve satisfying accuracy '''and''' speed.}}
{{NB|mind|The cutoff radius determines how many neighbor atoms <math>N_\mathrm{a}</math> are considered to describe each central atom's environment. Hence, important features may be missed if the cutoff radius is too small. On the other hand, a large cutoff radius increases the computational cost of the descriptor as the cutoff sphere contains more neighbor atoms. A good compromise is always system-dependent. Therefore, different values should be tested to achieve satisfying accuracy '''and''' speed.}}


The unit of the cut-off radius is <math>\AA</math>.
The unit of the cut-off radius is <math>\AA</math>.
The value of {{TAG|ML_RCUT1}} is the default value for {{TAG|ML_RCUT2}}.


== Related Tags and Sections ==
== Related tags and articles ==
{{TAG|ML_LMLFF}}, {{TAG|ML_RCUT2}}, {{TAG|ML_W1}}, {{TAG|ML_SION1}}, {{TAG|ML_SION2}}, {{TAG|ML_MRB1}}m {{TAG|ML_MRB2}}  
{{TAG|ML_LMLFF}}, {{TAG|ML_RCUT2}}, {{TAG|ML_W1}}, {{TAG|ML_SION1}}, {{TAG|ML_SION2}}, {{TAG|ML_MRB1}}, {{TAG|ML_MRB2}}  


{{sc|ML_RCUT1|Examples|Examples that use this tag}}
{{sc|ML_RCUT1|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 06:34, 11 May 2023

ML_RCUT1 = [real]
Default: ML_RCUT1 = 8.0 

Description: Sets the cutoff radius for the radial descriptor in .


The radial descriptor for machine-learned force fields 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; refer to the theory of machine-learned force fields for details. The tag ML_RCUT1 sets the cutoff radius at which the cutoff function decays to zero.

Mind: The cutoff radius determines how many neighbor atoms are considered to describe each central atom's environment. Hence, important features may be missed if the cutoff radius is too small. On the other hand, a large cutoff radius increases the computational cost of the descriptor as the cutoff sphere contains more neighbor atoms. A good compromise is always system-dependent. Therefore, different values should be tested to achieve satisfying accuracy and speed.

The unit of the cut-off radius is .

Related tags and articles

ML_LMLFF, ML_RCUT2, ML_W1, ML_SION1, ML_SION2, ML_MRB1, ML_MRB2

Examples that use this tag