An active learning workflow for predicting hydrogen atom adsorption energies on binary oxides based on local electronic transfer features

Machine learning combined with density functional theory (DFT) enables rapid exploration of catalyst descriptors space such as adsorption energy, facilitating rapid and effective catalyst screening. However, there is still a lack of models for predicting adsorption energies on oxides, due to the complexity of elemental species and the ambiguous coordination environment. This work proposes an active learning workflow (LeNN) founded on local electronic transfer features (e) and the principle of coordinate rotation invariance. By accurately characterizing the electron transfer to adsorption site atoms and their surrounding geometric structures, LeNN mitigates abrupt feature changes due to different element types and clarifies coordination environments. As a result, it enables the prediction of ^*H adsorption energy on binary oxide surfaces with a mean absolute error (MAE) below 0.18 eV. Moreover, we incorporate local coverage (θ_l) and leverage neutral network ensemble to establish an active learning workflow, attaining a prediction MAE below 0.2 eV for 5419 multi-^*H adsorption structures. These findings validate the universality and capability of the proposed features in predicting ^*H adsorption energy on binary oxide surfaces.