Image interpolation is a widely employed technique in face morphing and style transfer which allows for smooth transitions and blending of visual attributes between images. To achieve image interpolation, generative models can be used to learn the underlying distribution of data and generate new samples by linearly interpolating in the latent space. In this work, for selected zeolite structures from the IZA database, noise was added independently on two of the structures from t = 0 (pristine zeolite) to t = T/2 (half of the fully noisy state). And then, the grids of the two structures at t = T/2 were interpolated within the latent space and were subsequently denoised back to t = 0 (Figure ). Noise was only applied until t = T/2 since it loses all its information if extended to t = T. As depicted in Figure , the interpolated structures with different interpolation factors (λ) exhibited gradual transformations between two distinct structures, as evidenced by the smooth variation in visual characteristics such as the shape of energy grid and lattice parameters. Notably, defining an average or intermediate for molecules or crystalline materials is inherently challenging in material science. In this context, interpolation techniques utilizing generative models offer a potential solution to address this issue.