Predicting Adsorption Energies Using Multifidelity Data.

In this paper, we show that the binding energy of small adsorbates on transition-metal surfaces can be modeled to a high level of fidelity with data from multiple sources using multitask Gaussian processes (MT-GPs). This allows us to take 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. We report two case studies here-one using purely computational datasets and the other using a combination of experimental and computational datasets-to explore the performance of MT-GPs. In both cases, the performance of MT-GPs is significantly better than single-task models built on a single data source. We posit that this method can be used to learn improved models from fused datasets, thereby maximizing model accuracy under tight computational and experimental budget.