ML IWEIGHT: Difference between revisions
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*{{TAG|ML_FF_IWEIGHT}}=2: The training data are normalized by using their standard deviations. The averaging is done over whole training data. The normalized energy, forces and stress tensor are multiplied by {{TAG|ML_FF_WTOTEN}}, {{TAG|ML_FF_WTIFOR}} and {{TAG|ML_FF_WTSIF}}, respectively. In this case the flags {{TAG|ML_FF_WTOTEN}}, {{TAG|ML_FF_WTIFOR}} and {{TAG|ML_FF_WTSIF}} are unitless quantities. | *{{TAG|ML_FF_IWEIGHT}}=2: The training data are normalized by using their standard deviations. The averaging is done over whole training data. The normalized energy, forces and stress tensor are multiplied by {{TAG|ML_FF_WTOTEN}}, {{TAG|ML_FF_WTIFOR}} and {{TAG|ML_FF_WTSIF}}, respectively. In this case the flags {{TAG|ML_FF_WTOTEN}}, {{TAG|ML_FF_WTIFOR}} and {{TAG|ML_FF_WTSIF}} are unitless quantities. | ||
*{{TAG|ML_FF_IWEIGHT}}=3: Same as {{TAG|ML_FF_IWEIGHT}}=2 but training data is divided into individual subsets. | *{{TAG|ML_FF_IWEIGHT}}=3: Same as {{TAG|ML_FF_IWEIGHT}}=2 but training data is divided into individual subsets. | ||
The energies, forces and stress tensors for each subset are normalized using the average of the standard deviation of all subsets in the training data. The division into subsets is based on the name tag as given in the POSCAR file. | The energies, forces and stress tensors for each subset are normalized using the average of the standard deviation of all subsets in the training data. The division into subsets is based on the name tag as given in the {{TAG|POSCAR}} file. If training is performed for widely different materials, for instance different phases that have large energy differences, it is important to chose different names in the {{TAG|POSCAR}} file. If this is not done, the standard deviation for the energy might become large, reducing the weight of the energy equations. | ||
'''Mind''': For {{TAG|ML_FF_IWEIGHT}}=2 and 3 the weights are unitless quantities used to multiply the data, whereas for {{TAG|ML_FF_IWEIGHT}}=1 they have a unit. All three methods provide unitless energies, forces and stress tensors, that are passed | '''Mind''': For {{TAG|ML_FF_IWEIGHT}}=2 and 3 the weights are unitless quantities used to multiply the data, whereas for {{TAG|ML_FF_IWEIGHT}}=1 they have a unit. All three methods provide unitless energies, forces and stress tensors, that are passed | ||
Revision as of 05:52, 2 October 2020
ML_FF_IWEIGHT = [integer]
Default: ML_FF_IWEIGHT = 3
Description: Flag to control the weighting of training data in the machine learning force field method.
The following cases for ML_FF_IWEIGHT are possible:
- ML_FF_IWEIGHT=1: The unnormalized energies, forces and stress tensor training data are divided by the weights determined by the flags ML_FF_WTOTEN (eV/atom), ML_FF_WTIFOR (eV/Angstrom) and ML_FF_WTSIF (kBar), respectively.
- ML_FF_IWEIGHT=2: The training data are normalized by using their standard deviations. The averaging is done over whole training data. The normalized energy, forces and stress tensor are multiplied by ML_FF_WTOTEN, ML_FF_WTIFOR and ML_FF_WTSIF, respectively. In this case the flags ML_FF_WTOTEN, ML_FF_WTIFOR and ML_FF_WTSIF are unitless quantities.
- ML_FF_IWEIGHT=3: Same as ML_FF_IWEIGHT=2 but training data is divided into individual subsets.
The energies, forces and stress tensors for each subset are normalized using the average of the standard deviation of all subsets in the training data. The division into subsets is based on the name tag as given in the POSCAR file. If training is performed for widely different materials, for instance different phases that have large energy differences, it is important to chose different names in the POSCAR file. If this is not done, the standard deviation for the energy might become large, reducing the weight of the energy equations.
Mind: For ML_FF_IWEIGHT=2 and 3 the weights are unitless quantities used to multiply the data, whereas for ML_FF_IWEIGHT=1 they have a unit. All three methods provide unitless energies, forces and stress tensors, that are passed to the regression.
Related Tags and Sections
ML_FF_LMLFF, ML_FF_WTOTEN, ML_FF_WTIFOR, ML_FF_WTSIF