We show that this method results in improvements in computational efficiency, generalizability, and simplicity which can be leveraged to reduce the complexity of models needed for analysis of molecular simulation data (e.g., 3D convolutional neural networks are reduced to linear regression models). These improvements also reduce the need for large labeled datasets needed for ML model training and provide physical intuition. This provides opportunities to improve the information gained from the analysis of highthroughput or large-scale simulation data, which can be used in screening for new materials and chemistry or in optimizing physical and chemical characteristics of existing systems.