Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals.

ABSTRACT: The rational design of heterogeneous catalysts relies on the efficient survey of mechanisms by density functional theory (DFT). However, massive reaction networks cannot be sampled effectively as they grow exponentially with the size of reactants. Here we present a statistical principal component analysis and regression applied to the DFT thermochemical data of 71 C[Formula: see text]-C[Formula: see text] species on 12 close-packed metal surfaces. Adsorption is controlled by covalent ([Formula: see text]-band center) and ionic terms (reduction potential), modulated by conjugation and conformational contributions. All formation energies can be reproduced from only three key intermediates (predictors) calculated with DFT. The results agree with accurate experimental measurements having error

SUBMITTER: Garcia-Muelas R 

PROVIDER: S-EPMC6794282 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature