Predicting HOMO-LUMO Gaps Using Hartree-Fock Calculated Data and Machine Learning Models

The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation. In this study, we leveraged a data set of HOMO-LUMO gap values for small molecules obtained through Hartree-Fock (HF) calculations and developed ML models to predict HOMO-LUMO energy gaps for organic molecules. Molecular descriptors generated from Simplified Molecular Input Line Entry System (SMILES) representations using RDKit were used as input features to train various regression-based ML models. The data set included 46,717 small molecules with carbon chain number ranging from 1 to 8. Among the tested models, LightGBM regressor, Bidirectional LSTM, CatBoost regressor, and Multilayer Perceptron (MLP) achieved mean absolute error (MAE) values below 0.25 eV. Further improvement was achieved by creating a weighted ensemble model combining the LightGBM regressor, Bidirectional LSTM, and MLP, resulting in a prediction accuracy with an MAE of 0.1660 eV. This ensemble model outperformed others across various data sets, with the LightGBM regressor showing better performance for predicting the HOMO-LUMO gap of saturated linear molecules. SHAP analysis was conducted which identified 20 molecular descriptors critical for accurate predictions. Additionally, the models were empirically adapted to estimate experimental HOMO-LUMO gap values for both small and large molecules (up to carbon number 50), demonstrating their versatility and practical applicability.