Hydration induced mechanical degradation in the Y-doped BaZrO3 solid oxide

COMPUTATIONAL MATERIALS SCIENCE(2024)

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摘要
The hydration reaction leads to non-uniform stresses and strains inside solid oxide electrolytes, which seriously affects the mechanical properties of the material. Machine learning (ML) molecular dynamics (MD) provides an excellent means to investigate the effects of hydration on the structural and mechanical properties of electrolytes. In this paper, we develop an excellent ML potential with the accuracy of near first principles calculations and the efficiency of classical MD simulations to study the doping and hydration of BaZrO3 (BZO) materials. The lattice constants, elastic constants, thermal expansion coefficients (TEC) and melting point of BZO and the enthalpy of hydration of Y doped BaZrO3 (BZY) were calculated, and the results agree with the density functional theory (DFT) reference data, validating the accuracy of the ML potential. The study of the mechanical properties of BZY after the occurrence of the hydration reaction reveals that the Young's modulus and strength decrease significantly with the increase of the Y doping concentration, and the main reason for this decrease is the creation of oxygen vacancies by the doping process, which reduce the number of chemical bonds. The increase in hydration level also causes a decrease in the Young's modulus and strength, which is mainly attributed to the chemical expansion and weakening of the chemical bonds caused by protonic defects. Finally, the changes in Young's modulus and strength before and after hydration was also found to be only slightly different, indicating that the bond-strengthening effect induced by the filling of oxygen vacancies is compensated mainly by the bondweakening due to protonic defects. The research results can contribute to understanding material design rules to rationalize the chemical expansion to optimize the chemical mechanical response of oxide ceramics, ultimately developing more stable proton conductors for proton based solid oxide fuel cells and electrolytic cells.
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关键词
Machine learning potential,Molecular dynamics,Hydration,Chemical expansion,Mechanical property
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