Comparison of Ridge and Kernel Ridge Models in Predicting Thermal Stability of Zn-MOF Catalysts

Gustina Alfa Trisnapradika, Muhamad Akrom

Abstract


This study investigates machine learning-based quantitative structure-property relationship (QSPR) models for predicting the thermal stability of zinc metal-organic frameworks (Zn-MOF). Utilizing a dataset comprising 151 Zn-MOF compounds with relevant molecular descriptors, ridge (R) and kernel ridge (KR) regression models were developed and evaluated. The results demonstrate that the R model outperforms the KR model in terms of prediction accuracy, with the R model exhibiting exceptional performance (R² = 0.999, RMSE = 0.0022). While achieving high accuracy, opportunities for further improvement exist through hyperparameter optimization and exploration of polynomial functions. This research underscores the potential of ML-based QSPR models in predicting the thermal stability of Zn-MOF compounds and highlights avenues for future investigation to enhance model accuracy and applicability in materials science.

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References


T.S. Sreeprasad, P.M. Ajayan, High thermal stability of ultra-thin metal oxide nanowire networks: role of inter-wire coupling, Nanoscale, 4(2), 6732-6737 (2012).

M. Sahoo, Influence of magnesium doping on thermal stability, mechanical and biological properties of nano-hydroxyapatite, Materials Science and Engineering: C, 116, 111199 (2020).

R.E. Morris, P.S. Wheatley, Gas storage in nanoporous materials, Angewandte Chemie International Edition, 47(27), 4966-4981 (2008).

F.Zhou, A.J. Howarth, P.S. Mukherjee, High-resolution crystal structure analysis of ZIF-8: insights into the pore structure, stability, and interactions, Journal of the American Chemical Society, 137(26), 8928-8934 (2015).

L.E. Kreno, Metal–organic framework materials as chemical sensors, Chemical Reviews, 112(2), 1105-1125 (2011).

D. Farrusseng, Metal–organic frameworks: Applications from catalysis to gas storage, Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, 2(2), 153-168 (2010).

S.A. Morris, Metal–organic frameworks in luminescent sensing applications: Shining a light on MOF-based sensors, Coordination Chemistry Reviews, 307, 362-385 (2016).

J. Cui, H. Xu, A review of metal–organic frameworks for capturing and storing greenhouse gases, Chemical Society Reviews, 40(3), 1228-1246 (2011).

F.E. Abeng, V.C. Anadebe, Combined electrochemical, DFT/MD-simulation and hybrid machine learning based on ANN-ANFIS models for prediction of doxorubicin drug as corrosion inhibitor for mild steel in 0.5 M H2SO4 solution, Comput Theor Chem, 1229, 114334 (2023), https://doi.org/10.1016/J.COMPTC.2023.114334.

S.K. Elsaidi, High-throughput characterization of crystalline nanoporous materials, Angewandte Chemie International Edition, 5(46), 14981-14985 (2018).

M. Moharramnejad, L. Tayebi, Ali.R. Akbarzadeh, A. Maleki, A simple, robust, and efficient structural model to predict thermal stability of zinc metal-organic frameworks (Zn-MOFs): The QSPR approach, Microporous and Mesoporous Materials, 336, 111815 (2022), https://doi.org/10.1016/j.micromeso.2022.111815.

M. Akrom, S. Rustad, A.G. Saputro, A. Ramelan, F. Fathurrahman, H.K. Dipojono, A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds, Mater Today Commun, 35, 106402 (2023), https://doi.org/10.1016/J.MTCOMM.2023.106402.

M. Akrom, T. Sutojo, Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin Investigation of QSPR-Based Machine Learning Models in Pyrimidine Corrosion Inhibitors, Eksergi, 20(2), 107-111 (2023), https://doi.org/10.31315/e.v20i2.9864.

S. Budi, M. Akrom, H. Al Azies, U. Sudibyo, T. Sutojo, G.A. Trisnapradika, A.N. Safitri, A. Pertiwi, S. Rustad, Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors, KnE Engineering, 78-87 (2024), https://doi.org/10.18502/keg.v6i1.15351.

M. Akrom, S. Rustad, H.K. Dipojono, Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors, Results Chem, 6, 101126 (2023), https://doi.org/10.1016/J.RECHEM.2023.101126.

M. Akrom, S. Rustad, A.G. Saputro, H.K. Dipojono, Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors, Comput Theor Chem, 1229, 114307 (2023), https://doi.org/10.1016/J.COMPTC.2023.114307.

W. Herowati, W.A.E. Prabowo, M. Akrom, T. Sutojo, N.A. Setiyanto, A.W. Kurniawan, N.N. Hidayat, S. Rustad, Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms, KnE Engineering, 68-77 (2024), https://doi.org/10.18502/keg.v6i1.15350.

M. Akrom, S. Rustad, A.G. Saputro, A. Ramelan, F. Fathurrahman, H.K. Dipojono, A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds, Mater Today Commun, 35, 106402 (2023), https://doi.org/10.1016/J.MTCOMM.2023.106402.

M. Akrom, S. Rustad, H.K. Dipojono, Prediction of Anti-Corrosion performance of new triazole derivatives via Machine learning, Comp and Theoretical Chem, 1236, 114599 (2024), https://doi.org/10.1016/j.comptc.2024.114599.

M. Akrom, S. Rustad, H.K. Dipojono, A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors, Phys Scr, 99,(3), 036006 (2024), https://doi.org/10.1088/1402-4896/ad28a9.




DOI: https://doi.org/10.62411/jimat.v1i1.10542

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Journal of Multiscale Materials Informatics (JIMAT) published by Universitas Dian Nuswantoro, Semarang, Indonesia, and collaborates with Research Center for Materials Informatics.

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