Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments

Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of