Theoretical design of dendrite-free zinc anode through intrinsic descriptors from symbolic regression

Abstract

The growth of zinc dendrites limits the practical application of zinc ion batteries, which can be effectively suppressed by surface doping. Herein, the density functional theory combined with symbolic regression algorithm had been used to study the growth of zinc nuclei on 22 single atom-doped surfaces. The results indicate that the doping surfaces with convex structure can suppress the zinc dendrite growth because of the weak adsorption energy and low diffusion activation energy of zinc atoms. Moreover, the diffusion activation energy and the orientation of zinc nucleation depend on the adsorption energy of the first zinc atom. The larger the adsorption energy, the greater the diffusion barrier of zinc atoms and the greater the tendency for vertical growth of zinc nuclei. Therefore, the symbolic regression algorithm was utilized to identify the relationship between the adsorption energy of the first zinc atom and the properties of the doped atom. It was found that the radius and d-band center of doped atoms are key factors affecting the adsorption energy of the first zinc atom, and the doped atom with large atom radius and low d-band center can inhibit the zinc dendrite growth. Finally, the Al, Ag, Cd, In, Sn, Au, Hg, Tl, and Bi atoms are screened out to be the promising doping single atoms that can suppress the zinc dendrite growth.

Keywords

Surface doping / adsorption energy / diffusion activation energy / zinc nucleation / symbolic regression

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