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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam
Ananta Surya Pratama
Universitas Dian Nuswantoro
Computer Science & IT

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A Robust Voting Ensemble Framework for Predicting Thermal Stability in Zn-Based Metal–Organic Frameworks Taufiqul Umam; Harun Al Azies; Muhamad Akrom; Ananta Surya Pratama; Muhammad Diva Irnanda
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12748

Abstract

The prediction of thermal stability (TS) in zinc-based metal–organic frameworks (Zn-MOFs) is often challenged by experimental cost and distributional heterogeneity in materials datasets. This study proposes a median-based robust voting ensemble to model the TS of 151 Zn-MOF samples using four structural descriptors, nN, nZn, Het, and Lig. The framework integrates five robust linear estimators and is benchmarked against a linear kernel Support Vector Regression (SVR) model to evaluate predictive stability and generalization performance. The proposed ensemble demonstrates superior test performance (R² = 0.9986; RMSE = 0.0023) compared to SVR (R² = 0.9492; RMSE = 0.0213), indicating enhanced robustness under heterogeneous data conditions. Feature importance analysis identifies nitrogen coordination density and heteroatomic environment as the dominant contributors to TS prediction, while zinc center quantity and ligand topology exhibit comparatively minor influence. These findings confirm that median-based robust aggregation improves predictive reliability and provides chemically interpretable insight, offering a data-driven approach for the rational design and screening of thermally stable Zn-MOF materials.
Integration of Ensemble Stacking in Machine Learning for Thermal Stability Prediction of Metal-Organic Frameworks (MOF) Ananta Surya Pratama; Muhammad Diva Irnanda; Taufiqul Umam; Dandy Prasetyo Nugroho; Harun Al Azies
SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 14 No. 2 (2025): Volume 14 Nomor 2 (September 2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/mg9zs075

Abstract

Metal–Organic Frameworks (MOF) are porous materials with broad potential in energy, catalysis, and gas storage applications; however, their thermal stability remains a critical challenge. This study aims to develop a predictive model for the thermal stability of Zn-MOF based on the Quantitative Structure–Property Relationship (QSPR) using a stacking ensemble regression approach. The model was constructed with six base algorithms—Gaussian Process, KNN Regression, Ridge Regression, Linear Regression, Lasso Regression, and Support Vector Regression (SVR)—which were subsequently combined through TheilSen Regression as a meta-learner. The evaluation results demonstrate excellent predictive performance, with an MAE of 0.0022, an RMSE of 0.0023, and an R² of 0.9994 on the test set. Validation against experimental data also revealed only a tiny prediction error, confirming the model’s ability to accurately represent the complex relationships between structural descriptors and thermal stability. Compared to single regression models, the stacking approach proved to be more stable, robust against outliers, and adaptive to heterogeneous data distributions. These findings highlight the potential of stacking ensembles as an efficient data-driven material design strategy, enabling faster identification and design of thermally stable MOF while reducing reliance on costly and time-consuming laboratory experiments.
Co-Authors Dandy Prasetyo Nugroho Harun Al Azies Muhamad Akrom Muhammad Diva Irnanda Taufiqul Umam