This paper presents an implementation of an implicit solvation model into the widely used density-functional theory (DFT) code VASP. The model, based on joint density functional theory (JDFT), accounts for the effects of electrostatics, cavitation, and dispersion on the interaction between a solute and a solvent. The authors describe the theoretical framework, implementation details, and benchmark results for small molecular systems. They apply the solvation model to study the surface energies of different facets of semiconducting and metallic nanocrystals and the energy barrier for the Sn2 reaction pathway. The results show that solvation reduces the surface energies of nanocrystals, particularly for semiconductors, and increases the energy barrier for the Sn2 reaction. The implementation is validated through comparisons with other methods and experimental data, demonstrating its accuracy and efficiency in handling large periodic systems. The software is freely available as a patch to the original VASP code.This paper presents an implementation of an implicit solvation model into the widely used density-functional theory (DFT) code VASP. The model, based on joint density functional theory (JDFT), accounts for the effects of electrostatics, cavitation, and dispersion on the interaction between a solute and a solvent. The authors describe the theoretical framework, implementation details, and benchmark results for small molecular systems. They apply the solvation model to study the surface energies of different facets of semiconducting and metallic nanocrystals and the energy barrier for the Sn2 reaction pathway. The results show that solvation reduces the surface energies of nanocrystals, particularly for semiconductors, and increases the energy barrier for the Sn2 reaction. The implementation is validated through comparisons with other methods and experimental data, demonstrating its accuracy and efficiency in handling large periodic systems. The software is freely available as a patch to the original VASP code.