Computational prediction of sublimation enthalpy of organic compounds using the Monte Carlo technique

Sublimation enthalpy is a key property of organic compounds, influenced by intermolecular interactions and challenging to predict for new compounds lacking known crystal structures. This study developed a QSPR model to reliably estimate sublimation enthalpy in organic compounds using advanced molecular modeling techniques and Monte Carlo-based optimization. Trained on 1300 compounds, the model achieved high predictive accuracy (R²_train = 0.98, R²_val = 0.99 and RMSE_(train) = 2.88 KJ/mol, RMSE_(val) = 5.2306 KJ/mol for split 7). Mechanistic insights revealed that certain structural features such as double bonds and specific atoms increase sublimation enthalpy. These findings provide valuable guidance for designing organic compounds with desirable properties.