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 | 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 | 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| | {{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.