Now, the ML model only has to predict the variation in energy of the different configurations for each concentration and system size. This approach to scaling and segmenting data by concentration and system size introduces a new application for ranking algorithms, an area yet unexplored in materials science. As the name suggests, these algorithms label different data points with a ranking score rather than an explicit regression value. To this end, we have employed a categorical boosting algorithm, which is a decision tree-based ensemble (boosting) method. Catboost is known for its accuracy and generalizability across data.