MEP-CNN: A Visual and Explainable Machine Learning Framework for Explosive Molecule Classification based on Molecular Electrostatic Potentials

Identifying explosive molecules is crucial for safety and materials research. In this work, we propose a machine learning framework for classifying 119 molecules using images of their electrostatic potential maps (MEPs), computed from quantum-chemical methods and depicting the molecules' positive and negative electric charge regions. The training dataset was augmented by rotating each image 30 times, and a Convolutional Neural Network (CNN) was subsequently trained to classify molecules. The CNN classification model was combined with advanced gradient-based Explainable Artificial Intelligence (XAI) techniques and visualization methods. The CNN achieved 100% accuracy on the test set, demonstrating its effectiveness. In a separate experiment, it achieved a 81.25% accuracy using molecules chemically distinct from the training set: the CNN model classified eight as explosive, including two known simulants and two non-explosive compounds containing strong electron-withdrawing groups, thus indicating sensitivity to chemically relevant features not included in the training distribution. To gain a deeper understanding of the model’s decision-making process, we used the Saliency Map technique with the reduce_max function to identify the most critical regions in the MEP images for the classification task. Neutral and homogeneous MEP regions primarily characterize non-explosive molecules . In contrast, explosive compounds are associated with pronounced electrostatic potential gradients and localized charge features , particularly in regions related to nitro groups and trigger bonds. This work highlights the potential of combining MEPs computed from quantum-chemical methods with machine learning to analyze and classify molecules with targeted properties.