  In this paper, we apply multi-task Gaussian Process (MT-GP) to show that the
adsorption energy of small adsorbates on transition metal surfaces can be
modeled to a high level of fidelity using data from multiple sources, taking
advantage of the relatively abundant ''low fidelity" data (such as from density
functional theory computations) and small amounts of ''high fidelity"
computational (e.g. using the random phase approximation) or experimental data.
To fully explore the performance of MT-GP, we perform two case studies - one
using purely computational datasets and the other using a combination of
experimental and computational datasets. In both cases, the performance of
MT-GPs is significantly better than single-task models built on a single data
source. This method can be used to learn improved models from fused datasets,
and thereby build accurate models under tight computational and experimental
budget.
