Dissociative Electron Attachment Prediction of Halogenated Organic Molecules Using Machine Learning

Dissociative Electron Attachment (DEA) is a fundamental process in which low-energy electrons interact with molecules, causing bond dissociation and the formation of negative ions. It plays a key role in environmental science, nanotechnology, biology, and astrochemistry. However, experimental DEA studies are typically conducted in the gas phase under high-vacuum conditions, limiting the investigation of larger or less volatile molecules. To address these limitations, we developed machine learning (ML) models to predict negative ion formation in halogenated organic molecules. Classification models were designed to predict the energy range of the most intense DEA resonance, while regression models estimate its peak energy. A relational database consolidating experimental DEA data and molecular descriptors for 143 molecules served as the basis for model training and evaluation. Various ML approaches spanning different algorithm families were compared, using 120 molecules for training and 23 for testing. The ensemble Voting Classifier, combining three models from different families, achieved 94.9% accuracy in cross-validation with only one test set misclassification. The Random Forest model achieved the best regression results with a mean absolute error of 0.301 eV in cross-validation and 0.234 eV on the test set, comparable to typical experimental resolutions. These results demonstrate the feasibility of ML-based DEA prediction, establishing a foundation for computational approaches capable of expanding research to molecules that are challenging to study experimentally.