The standard approach of using all-atom MD simulations to reveal useful properties of a system (with or without the application of Machine Learning or ML) involves employing high-domain knowledge to the MD-simulation-derived trajectory information. For example, in several of our previous papers, we have utilized all-atom MD simulation generated trajectories for computing the water properties such as the tetrahedral order parameter (q) or dipole moment distribution (𝜃) inside the PMETAC brush layer. In fact, in one of these studies, we employed a ML protocol (a combination of Gaussian pre-processing and unsupervised clustering algorithm) on such allatom MD generated information on q and 𝜃 of water molecules surrounding the {N(CH3)3} + group of the PMETAC brushes and demonstrated the occurrence of the multiple hydration states of the {N(CH3)3} + group.