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<p>This paper presents three robust methods based on a Quantitative Structure-Property Relationship (QSPR) model. These methods are designed to effectively identify high-energy metal-organic frameworks (HE-MOFs) incorporating tetrazole ligands. The methods employ topology, spatial, and structural descriptors through multiple linear regression (MLR) models. The study highlights key physicochemical parameters of HE-MOFs, including density (<i>ρ</i>), enthalpy of formation <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{({\Delta\:}\text{H}}_{\text{f}}^{^\circ\:})\)</EquationSource> </InlineEquation>, and decomposition temperature (<i>T</i><sub>dec</sub>). The new model demonstrates significant improvements over previous approaches, achieving high determination coefficients of 0.904, 0.974, and 0.947, respectively. It also showcases superior predictive accuracy, validated through cross-validation and external validation techniques. The statistical results, such as <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{Q}_{LOO}^{2}\:\)</EquationSource> </InlineEquation>and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:{Q}_{LMO}^{2}\)</EquationSource> </InlineEquation>values—0.966 and 0.972 for <i>ρ</i>, 0.974 and 0.976 for <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\:{{\Delta\:}\text{H}}_{\text{f}}^{^\circ\:}\)</EquationSource> </InlineEquation>, and 0.986 and 0.974 for <i>T</i><sub>dec</sub>—confirm the model’s robustness, reliability, and effectiveness in designing high-performance HE-MOFs.</p> Graphical Abstract <p></p>

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Prediction of the Enthalpy of Formation, Density, and Thermal Decomposition Temperature of Tetrazole-Based Metal-Organic Frameworks in order To Understand their Behavior as New Generation of Energetic Materials

  • 期刊论文

  • 发表日期：

    2025年6月30日

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  • Nargas. Zohari,
  • Zeinab Mohammadpour,
  • Mohammad Ali Zarei,
  • Mojtaba Mahyari

摘要

This paper presents three robust methods based on a Quantitative Structure-Property Relationship (QSPR) model. These methods are designed to effectively identify high-energy metal-organic frameworks (HE-MOFs) incorporating tetrazole ligands. The methods employ topology, spatial, and structural descriptors through multiple linear regression (MLR) models. The study highlights key physicochemical parameters of HE-MOFs, including density (ρ), enthalpy of formation \(\:{({\Delta\:}\text{H}}_{\text{f}}^{^\circ\:})\) , and decomposition temperature (Tdec). The new model demonstrates significant improvements over previous approaches, achieving high determination coefficients of 0.904, 0.974, and 0.947, respectively. It also showcases superior predictive accuracy, validated through cross-validation and external validation techniques. The statistical results, such as \(\:{Q}_{LOO}^{2}\:\) and \(\:{Q}_{LMO}^{2}\) values—0.966 and 0.972 for ρ, 0.974 and 0.976 for \(\:{{\Delta\:}\text{H}}_{\text{f}}^{^\circ\:}\) , and 0.986 and 0.974 for Tdec—confirm the model’s robustness, reliability, and effectiveness in designing high-performance HE-MOFs.

Graphical Abstract