ML RDES SPARSDES: Difference between revisions

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{{TAGDEF|ML_RDES_SPARSDES|[real]|0.5}}
{{TAGDEF|ML_RDES_SPARSDES|[real]|0.5}}


Description: Sets the ratio of descriptors removed during angular-descriptor sparsification.
Description: Sets the ratio of descriptors kept during angular-descriptor sparsification.
{{NB|mind|This tag is only available as of VASP 6.4.3.}}
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During [[Machine_learning_force_field:_Theory#Sparsification_of_angular_descriptors|angular-descriptor sparsification]] ({{TAG|ML_LSPARSDES}}=T), insignificant angular descriptors are removed based on a leverage scoring. The percentage of angular descriptors that are removed is determined by the value of {{TAG|ML_RDES_SPARSDES}}, which must be chosen between <math>0 < r \leq 1</math>. In practice, we recommend scanning a range between 0.1 to 0.9. Removing angular descriptors increases the performance of a force field, but it decreases accuracy at the same time. One method to find the optimal tradeoff between accuracy and performance is to do a Pareto front with run time on the x-axis and accuracy on the y-axis.
During [[Machine_learning_force_field:_Theory#Sparsification_of_angular_descriptors|angular-descriptor sparsification]] ({{TAG|ML_LSPARSDES}}=T), insignificant angular descriptors are removed based on a leverage scoring. The percentage of angular descriptors that are kept is determined by the value of {{TAG|ML_RDES_SPARSDES}}, which must be chosen between <math>0 < r \leq 1</math>. In practice, we recommend scanning a range between 0.1 to 0.9. Removing angular descriptors increases the performance of a force field, but it decreases accuracy at the same time. One method of finding the optimal tradeoff between accuracy and performance is to do a Pareto front with run time on the x-axis and accuracy on the y-axis.


== Related tags and articles ==
== Related tags and articles ==
{{TAG|ML_LSPARSDES}}, {{TAG|ML_NRANK_SPARSDES}}, {{TAG|ML_LMLFF}}
{{TAG|ML_LMLFF}}, {{TAG|ML_LSPARSDES}}, {{TAG|ML_NRANK_SPARSDES}}, {{TAG|ML_DESC_TYPE}}


{{sc|ML_EPS_LOW|Examples|Examples that use this tag}}
{{sc|ML_EPS_LOW|Examples|Examples that use this tag}}


[[Category:INCAR tag]][[Category:Machine-learned force fields]]
[[Category:INCAR tag]][[Category:Machine-learned force fields]]

Latest revision as of 14:01, 20 March 2024

ML_RDES_SPARSDES = [real]
Default: ML_RDES_SPARSDES = 0.5 

Description: Sets the ratio of descriptors kept during angular-descriptor sparsification.

Mind: This tag is only available as of VASP 6.4.3.

During angular-descriptor sparsification (ML_LSPARSDES=T), insignificant angular descriptors are removed based on a leverage scoring. The percentage of angular descriptors that are kept is determined by the value of ML_RDES_SPARSDES, which must be chosen between . In practice, we recommend scanning a range between 0.1 to 0.9. Removing angular descriptors increases the performance of a force field, but it decreases accuracy at the same time. One method of finding the optimal tradeoff between accuracy and performance is to do a Pareto front with run time on the x-axis and accuracy on the y-axis.

Related tags and articles

ML_LMLFF, ML_LSPARSDES, ML_NRANK_SPARSDES, ML_DESC_TYPE

Examples that use this tag