Modifying surrogate model¶
This tutorial extends the previous one for TiO clusters, searching for TiO 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:
search = GOFEE(calc=calc,
startgenerator=sg,
candidate_generator=candidate_generator,
max_steps=100,
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. As the regularization is a parameter of the kernel, passed to the GPR model, the code will look like this:
from surrogate.gpr import GPR
from surrogate.kernel import double_gauss_kernel
kernel = double_gauss_kernel(noise=1e-6)
gpr = GPR(kernel=kernel)
search = GOFEE(calc=calc,
gpr=gpr,
startgenerator=sg,
candidate_generator=candidate_generator,
max_steps=100,
population_size=5)