As mentioned previously, figuring out this complex physics governing the EOS transport in the PMETA-brush-grafted nanochannels consumed a significant amount of energy, time, and computational resources. This prompted us to ponder this question: Is there a logical way to narrow down the analysis space for this problem so that it becomes possible to explain the mechanism of non-linear EOS transport in PMETAC-brush-grafted nanochannels much more promptly? A common way of extracting information from a complex system is to apply some sort of perturbation and observe changes in the variables or features of interest. However, finding appropriate features is a challenging task. Creating these features or variables that describe the state of a system requires a lot of subject or domain knowledge. For example, consider the tetrahedral order parameter or q parameter of water molecules. This parameter was developed to discuss how close to a tetrahedron-like structure a water molecule and its surrounding other water molecules are. The functional form of the q parameter required a lot of domain knowledge such as the fact that the water is a polar molecule, water creates four hydrogen bonds with its four neighbors, the information on the structure of water and its electronic structure, the fact that it acts as a donor to two of its neighboring water molecules and acceptor to the other twos, etc. Compared to water, the PE brush is a much more complex system. When analyzing such systems, it is not generally known apriori to the researcher what variables to use that have a good physical meaning or can help to extract the appropriate physics or give some sort of information about the underlying mechanism that governs such systems. Especially for a complex molecular system, finding a suitable and meaningful variable that navigates through the analysis space is a daunting task. It would be of great value if there exists a protocol or an analysis method that is grounded on solid physical arguments. We tried to tackle this class of problem, and our general philosophical argument is that many simple variables (that don't require high domain intelligence), when combined with an appropriate machine learning (ML) algorithm, can be used to extract meaningful information about a complex molecular system. Many variables generate a high dimensional data space, which naturally motivated us to look into the available machine learning (ML) algorithms capable of analyzing and handling such high-dimensional data. However, the journey from transforming the raw data to the data used into the ML algorithm, making an appropriate choice of the ML algorithm, and identifying the right procedures to interpret the data is a long one that must be supported by solid physics-based arguments. It should also have some form of generality to it, so that the same protocol or some slight modification of it could be used for other systems as well.