• Gaussian Mixture Model (GMM): This method can be seen as a generalization of K-Means in the sense that each cluster is described by a Gaussian distribution, allowing for more flexible cluster shapes, such as ellipses, with varying size, orientation, and covariance. Unlike K-Means, which uses hard assignments, GMM employs a soft (probabilistic) approach, where each data point is assigned a probability of belonging to multiple clusters. This results in a more flexible clustering approach, as data points can partially belong to different clusters. GMM is particularly suited for datasets where clusters overlap or exhibit complex shapes, making it a more versatile method for capturing non-spherical structures. Because GMM learns the underlying data distribution, it can also be used as a generative model to sample geometries from specific regions of the NAMD trajectory space.