  We explore the use of non homogenous noise kernels in Gaussian process
modelling to improve the potential energy curve models describing stochastic
electronic structure data. We use the same noise kernels on energy curves
describing deterministic electronic structure data by creating non-homogenous
noise model. We observe, as well as agreement between the noise of the model
and the stochastic data, a strong regularisation of the curves when using
artificial noises on deterministic data which improves Gaussian processes in
the over fitting regime.
