Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions.

摘要: The permutation invariant polynomial-neural network (PIP-NN) method for constructing highly accurate potential energy surfaces (PESs) gas phase molecules is extended to molecule-surface interaction PESs. symmetry adaptation in the NN fitting of a PES achieved by employing as input functions that fulfill both translational surface and molecule. These are low-order PIPs primitive containing periodic symmetry. It stressed permutationally cross terms needed avoid oversymmetrization. accuracy efficiency demonstrated model H2 + Cu(111) system density functional theory points Ag(111) system.