1. Department of Physics, Materials and Energy Research Center, Dez. C., Islamic Azad University, Dezful, Iran
10.22034/ijche.2026.573439.1582
Abstract
Aqueous stability of metal-organic frameworks (MOFs) remains a major challenge for their industrial application. This study presents a hybrid approach combining reactive molecular dynamics (ReaxFF-MD) and machine learning to predict hydrolytic stability. Simulations for six MOF structures at six humidity levels generated 12 samples. Instability was defined as irreversible reduction in metal-ligand bonds or cell volume lasting ≥50 picoseconds. Two dynamic features, enthalpy variance (σH²) and potential energy change rate (rE), were extracted from the initial 200 picoseconds, while ΔV was removed to prevent data leakage. The logistic regression model, with hyperparameter optimization and LOMOO-CV validation, achieved 94% accuracy, Matthews correlation coefficient of 0.85, and ROC-AUC of 0.96. Due to limited data, results are reported as proof-of-concept with 95% confidence intervals via bootstrapping. Findings demonstrate that dynamic features have significantly higher predictive power for MOF water stability than static features.
Ghavaminia,H . (2026). Machine Learning-Assisted Prediction of MOF Hydrolytic Stability from Reactive Molecular Dynamics Simulations. (e246586). Iranian Chemical Engineering Journal, (), e246586 doi: 10.22034/ijche.2026.573439.1582
MLA
Ghavaminia,H . "Machine Learning-Assisted Prediction of MOF Hydrolytic Stability from Reactive Molecular Dynamics Simulations" .e246586 , Iranian Chemical Engineering Journal, , , 2026, e246586. doi: 10.22034/ijche.2026.573439.1582
HARVARD
Ghavaminia H. (2026). 'Machine Learning-Assisted Prediction of MOF Hydrolytic Stability from Reactive Molecular Dynamics Simulations', Iranian Chemical Engineering Journal, (), e246586. doi: 10.22034/ijche.2026.573439.1582
CHICAGO
H Ghavaminia, "Machine Learning-Assisted Prediction of MOF Hydrolytic Stability from Reactive Molecular Dynamics Simulations," Iranian Chemical Engineering Journal, (2026): e246586, doi: 10.22034/ijche.2026.573439.1582
VANCOUVER
Ghavaminia H. Machine Learning-Assisted Prediction of MOF Hydrolytic Stability from Reactive Molecular Dynamics Simulations. Iranian Chemical Engineering Journal. 2026;():e246586 (In Persian). doi: 10.22034/ijche.2026.573439.1582
