ML_LOGFILE 0.2.1
 
 
* MEMORY INFORMATION ***********************************************************************************************************************
 
Estimated memory consumption for ML force field generation (MB):
 
Persistent allocations for force field        :    546.9
|
|-- CMAT for basis                            :     18.7
|-- FMAT for basis                            :    508.4
|-- DESC for basis                            :      2.7
|-- DESC product matrix                       :      1.6
 
Persistent allocations for ab initio data     :     13.5
|
|-- Ab initio data                            :     12.9
|-- Ab initio data (new)                      :      0.5
 
Temporary allocations for sparsification      :    170.1
|
|-- SVD matrices                              :    170.0
 
Other temporary allocations                   :     34.8
|
|-- Descriptors                               :     11.1
|-- Regression                                :     12.0
|-- Prediction                                :     11.7
 
Total memory consumption                      :    765.3
 
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* MACHINE LEARNING SETTINGS ****************************************************************************************************************
 
This section lists the available machine-learning related settings with a short description, their
selected values and the INCAR tags. The column between the value and the INCAR tag may contain a
"state indicator" highlighting the origin of the value. Here is a list of possible indicators:
 
 *     : (empty) Tag was not provided in the INCAR file, a default value was chosen automatically.
 * (I) : Value was provided in the INCAR file.
 * (i) : Value was provided in the INCAR file, deprecated tag.
 * (!) : A value found in the INCAR file was overwritten by the contents of the ML_FF or ML_AB file.
 * (?) : The value for this tag was never set (please report this to the VASP developers).
 
Tag values with associated units are given here in Angstrom/eV, if not specified otherwise.
 
Please refer to the VASP online manual for a detailed description of available INCAR tags.
 
 
General settings
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Machine learning operation mode in strings (supertag)                                                 :          NONE     ML_MODE                 
Machine learning operation mode                                                                       :             0 (I) ML_ISTART               
Precontraction of weights on Kernel for fast execution (ML_ISTART=2 only), but no error estimation    :             F     ML_LFAST                
Controls the verbosity of the output at each MD step when machine learning is used                    :             1     ML_OUTPUT_MODE          
Sets the output frequency at various places for ML_ISTART=2                                           :             1     ML_OUTBLOCK             
 
 
Descriptor settings
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Radial descriptors:
-------------------
Cutoff radius of radial descriptors                                                                   :   8.00000E+00     ML_RCUT1                
Gaussian width for broadening the atomic distribution for radial descriptors                          :   5.00000E-01     ML_SION1                
Number of radial basis functions for atomic distribution for radial descriptors                       :            12     ML_MRB1                 
 
Angular descriptors:
--------------------
Descriptor type (standard, linear-scaling with element types, ...)                                    :             0     ML_DESC_TYPE            
Cutoff radius of angular descriptors                                                                  :   5.00000E+00     ML_RCUT2                
Gaussian width for broadening the atomic distribution for angular descriptors                         :   5.00000E-01     ML_SION2                
Number of radial basis functions for atomic distribution for angular descriptors                      :             8     ML_MRB2                 
Maximum angular momentum quantum number of spherical harmonics used to expand atomic distributions    :             3     ML_LMAX2                
Angular filtering enabled                                                                             :             T     ML_LAFILT2              
Angular filtering parameter a_FILT                                                                    :   2.00000E-03     ML_AFILT2               
Angular filtering function type                                                                       :             2     ML_IAFILT2              
Enable sparsification of angular descriptors                                                          :             F     ML_LSPARSDES            
Number of highest eigenvalues relevant in the sparsification algorithm of the angular descriptors     :             5     ML_NRANK_SPARSDES       
Desired ratio of selected to all descriptors resulting from the angular descriptor sparsification     :   5.00000E-01     ML_RDES_SPARSDES        
 
 
Kernel settings
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Weight of radial descriptors in the kernel (the angular counterpart is chosen so that the sum is 1.0) :   1.00000E-01     ML_W1                   
Power of the polynomial kernel                                                                        :             4     ML_NHYP                 
Specifies whether super-vector is used for kernel or not                                              :             T     ML_LSUPERVEC            
 
 
Bayesian error estimation
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Enable automatic updating of the Bayesian error estimation threshold during on-the-fly training       :             1     ML_ICRITERIA            
Decides whether update of threshold is done in the same MD step or the next MD step                   :             1     ML_IUPDATE_CRITERIA     
Bayesian error estimation threshold (initial or static value depending on other settings)             :   2.00000E-03     ML_CTIFOR               
Scaling factor for ML_CTIFOR. The interval 0