Local potential energy density-supramolecular energy (LPED-SME) machine learning prediction – a web application to obtain the local SME from simple inputs

We developed a Flask web application that uses supervised machine learning (ML) to predict the local potential energy density (LPED) based on intermolecular and intramolecular interactions. The predictions are made from simple inputs, specifically the atomic charges of interacting atoms (using MK, ChelpG, or RESP schemes) and the interatomic distances between them. This application streamlines the process by avoiding the more complex calculations required by QTAIM topology. We optimized the size of our dataset to 53 samples, being a simple dataset with only three numerical features and no categorical features. We tested five different ML models and found that Linear Regression performed the best, achieving an R² score of 0.88, a mean absolute error (MAE) of 0.72 kcal/mol·Bohr³, a mean squared error (MSE) of 0.82 kcal²/mol²·Bohr⁶, and a root mean squared error (RMSE) of 0.91 kcal/mol·Bohr³. To ensure the reliability of our model, we conducted a secondary validation using a different set of input data with known LPED values. The predicted values closely matched the actual values, and the metrics from this secondary validation were similar to those from the primary testing. With this double validation, our web application is a reliable tool for obtaining LPED and local supramolecular energy (SME) from straightforward inputs. The major physical insight is the capability of the machine learning model to obtain a topologically derived information such as LPED using non-topological data.