Here, we propose an alternative: we show that we can employ a ML algorithm (namely Linear Discriminant Analysis or LDA) on very "basic" MD-derived features (or features representing "low-domain" knowledge) to generate information that replaces the need for high-domain knowledge for providing useful information on the system. In summary, therefore, we propose that ML-enabled low-domain knowledge can become equivalent to high-domain knowledge. Fig. provides the summary of the ML protocol that will be employed here. The protocol consists of five basic steps: (1) selection of basic features; (2) generating appropriate data for feeding into the ML algorithm; (3) transforming the data so that the ML algorithm (when applied to this data) can provide more effective predictions; (4) actual application of the ML algorithm (LDA) to project the high-dimensional dataset into low-dimensional spaces in a manner such that the datasets have maximum separation; (5)