Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal Surfaces

Abstract In the current work, we design a single unique machine learning (ML) algorithm capable of predicting the binding energies of several C, N and O‐based adsorbates and atomic hydrogen on different facets (100, 111, 211) of eleven transition‐metals considering an FCC bulk structure (Co, Rh, Ir, Ni, Pd, Pt, Ru, Os, Cu, Ag, Au) with high accuracy with respect to the reference DFT calculations. The selected properties/features are based on already available data or electronic properties easily obtained from DFT calculations of the free adsorbates and on the clean metal surfaces. The mean average error (MAE) for the training set is equal to 0.074 eV, while for the test set (data not used in training) is equal to 0.174 eV, thus having a very small error with respect to the reference DFT calculations. Further modifications of the present algorithm, which is based on an Extragradient boost (XGBoost) regressor in combination with a tree booster, with the addition of few more features might set the ground to develop ML algorithms able to predict the binding energies of adsorbates on more complex catalytic surfaces.