Kernel regression using a random kernel

Here we provide an example of input file to perform kernel noise ML (KNML) fit for W using a random kernel, in a single step (unlike the polynomial-kernel example above, no separate LML pre-fit or kernel point selection step is required here). The relevant files are provided in examples/kernel_r_fe_bso4.

&input_ml
debug=.false.

!ML mode
ml_type=1                  !set 1 to perform kernel fit
mld_order=2                !set 2 for quadratic regression

write_desc=.false.
desc_forces=.true.         !set true to fit the forces

!Define your system
weighted=.false.           !set true for multicomponent systems
chemical_elements=" Fe "   !provide the system composition

!Descriptor settings
r_cut=4.7d0                !set the cutoff distace Rc
descriptor_type=9          !set 9 for bispectrum SO4
j_max=4                    !angular moment for bispectrum SO4

!Kernel sparsification (if needed)
write_kernel_matrix=.false.
power_mcd=0.05d0
kernel_dump=3               !3 for dump by MCD/MAHALANOBIS

!Kernel settings
np_kernel_ref=2000          !number of proposed points in the MCD class
np_kernel_full=2000         !number of points outside the MCD class
kernel_type=7               !set 7 for a random kernel
sigma_kernel=0.02

&end