(), are transformed into birth-persistence pairs to emphasize the stability and prominence of features. These pairs are then convolved with Gaussian functions, smoothed, and discretized onto a fixed-resolution grid to create persistence images. This approach translates the structural and topological information of the crystal into a standardized vector format suitable for machine learning, enabling direct comparison across structures and efficient extraction of meaningful features.