ML RDES SPARSDES: Difference between revisions

Revision as of 09:43, 3 October 2023

ML_RDES_SPARSDES = [real]
Default: ML_RDES_SPARSDES = 0.5

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

During angular-descriptor sparsification (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 ML_RDES_SPARSDES, which must be chosen between $0 < r \leq 1$ . 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.

@@ Line 2: / Line 2: @@
 {{TAGDEF|ML_RDES_SPARSDES|[real]|0.5}}
-Description: This tag sets the ratio of the selected to the total number of descriptors within the angular descriptor sparsification (within the machine learning force field method).
+Description: Sets the ratio of descriptors removed during angular-descriptor sparsification.
 ----
-For details on the theory of angular descriptor sparsification see [[Machine learning force field: Theory#Sparsification of angular descriptors|here]].
-For details on how to use the sparsification see the tag {{TAG|ML_LSPARSDES}}.
+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.
-The values for {{TAG|ML_RDES_SPARSDES}} must be chosen between <math>0 < r \leq 1</math>.
 == Related tags and articles ==
-{{TAG|ML_LMLFF}}, {{TAG|ML_LSPARSDES}}, {{TAG|ML_NRANK_SPARSDES}}
+{{TAG|ML_LSPARSDES}}, {{TAG|ML_NRANK_SPARSDES}}, {{TAG|ML_LMLFF}}
 {{sc|ML_EPS_LOW|Examples|Examples that use this tag}}
 [[Category:INCAR tag]][[Category:Machine-learned force fields]]