Modifying surrogate model

This tutorial extends the previous one for Cu15 clusters. It is therefore recomended that you do that one before the present one.

In the avove mentioned tutorial GOFEE was initialized with the following arguments:

from gofee import GOFEE
search = GOFEE(calc=calc,
               startgenerator=sg,
               candidate_generator=candidate_generator,
               max_steps=60,
               population_size=5)

however GOFEE takes a number of other arguments, including a Gaussian Process regression (GPR) model, which is actively learned during the search and used for cheap optimization of new candidates.

One can for example apply a GPR model with another degree of regularization in the search. This is controlled by the noise parameter of the kernel, passed to the GPR model. The modification can be achieved by:

from gofee.surrogate import GPR
from gofee.surrogate.kernel import DoubleGaussKernel

kernel = DoubleGaussKernel(noise=1e-6)
gpr = GPR(kernel=kernel)

search = GOFEE(calc=calc,
               gpr=gpr,
               startgenerator=sg,
               candidate_generator=candidate_generator,
               max_steps=60,
               population_size=5)