As a first attempt at this class of problems, we came up with a strategy that is based on Linear Discriminant Analysis (LDA). In this strategy, we employ a ML method (e.g., LDA) on some very basic features derived from the MD simulation data; the result is that we effectively replace high-domain knowledge by employing ML on low-domain knowledge. Specifically, for this problem (namely, understanding the mechanisms enforcing a non-linearly large EOS transport in nanochannels grafted with chloride-ion-screened PMETA brushes), this basic feature is nx-y (number of "y" atoms around a chosen "x" atom). We choose several such basic features in different bins (in which the simulation domain has been divided) for the cases where the system (nanochannels grafted with chloride-ion-screened PMETA brushes) has been subjected to electric fields of different strengths: we identify one case as a reference case and other cases as perturbed cases. Subsequently, we employ the LDA on these features and obtain clusters (i.e., map the highdimensional data onto a lower dimensional space in such a way that the difference between the datapoints in the reference and the perturbed states is maximum) corresponding to each feature in each bin. Such low dimensional space that enables maximum separability is characterized by an axis that is obtained as a weighted combination of these features. These "weights" are the "importance score" of these different features. Hence, by identifying the appropriate axis that ensures the maximum separation between the data from the reference and perturbed states, we can identify the change in the "importance score" corresponding to the different features across the bins (for a given perturbation or a given change in electric field) or in a given bin for different electric fields (or perturbations). From that analysis (which tells us which feature's importance score undergoes the maximum change), we can pinpoint the appropriate basic feature that should be investigated at a specific location (or bin) in greater detail to better explain the non-linear increase in the EOS flow strength. Thus, our approach can pinpoint (and thereby significantly reduce the analysis space and hence the time and cost of that analysis) "what to study" and "where to study". We explain that using this approach we are able to guide the analysis towards probing in greater details the distributions of the monomers, chloride ions, and water molecules in the brush-bulk interface; accordingly, it becomes possible to quickly identify the role of the