Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks
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| Format: | Preprint |
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2026
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| author | Szewczyk, Aleksander Sandonas, Leonardo Medrano Bodesheim, David Mortazavi, Bohayra Cuniberti, Gianaurelio |
| author_facet | Szewczyk, Aleksander Sandonas, Leonardo Medrano Bodesheim, David Mortazavi, Bohayra Cuniberti, Gianaurelio |
| contents | Covalent Organic Frameworks (COFs) are versatile two-dimensional (2D) materials for flexible electronics, catalysis, and sensing, owing to their tunable architectures and large surface areas. However, like most materials, COFs inevitably contain synthesis-induced defects, which-similar to graphene-can strongly influence intrinsic properties, such as thermal transport and mechanical strength. To address this challenge, we have assessed the performance of a set of machine learning interatomic potentials (MLIP) capable of efficient large-scale simulations of COFs with quantum accuracy. In doing so, QCOF models (Quantum COF) were developed by tuning the state-of-the-art MACE architecture on an extensive dataset of non-equilibrium COF conformations generated from high-fidelity density functional theory calculations. The accuracy, computational efficiency, memory footprint, and transferability to unseen chemical environments of these models were benchmarked against general-purpose MACE models and their fine-tuned variants. Our results show that an invariant QCOF model with a small descriptor dimensionality and cutoff outperforms all other models in most validation tasks, including scalability to large systems, force prediction in defective COFs, and phonon dispersion calculations. The best-performing QCOF model was then used to run large-scale simulations of thermal conductivity for defective CTF-1 and COF-LZU1 systems via non-equilibrium MD, revealing a more pronounced sensitivity of CTF-1 to structural defects. Stress-strain curves were also investigated, showing that the mechanical response remains nearly invariant at low defect densities, while asymmetric behaviour emerges at large strains. This work thus provides a foundation for the design of robust quantum-informed MLIP for large-scale property simulations of defective of extended network materials. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_21494 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks Szewczyk, Aleksander Sandonas, Leonardo Medrano Bodesheim, David Mortazavi, Bohayra Cuniberti, Gianaurelio Materials Science Chemical Physics I.6.4; J.2; J.6 Covalent Organic Frameworks (COFs) are versatile two-dimensional (2D) materials for flexible electronics, catalysis, and sensing, owing to their tunable architectures and large surface areas. However, like most materials, COFs inevitably contain synthesis-induced defects, which-similar to graphene-can strongly influence intrinsic properties, such as thermal transport and mechanical strength. To address this challenge, we have assessed the performance of a set of machine learning interatomic potentials (MLIP) capable of efficient large-scale simulations of COFs with quantum accuracy. In doing so, QCOF models (Quantum COF) were developed by tuning the state-of-the-art MACE architecture on an extensive dataset of non-equilibrium COF conformations generated from high-fidelity density functional theory calculations. The accuracy, computational efficiency, memory footprint, and transferability to unseen chemical environments of these models were benchmarked against general-purpose MACE models and their fine-tuned variants. Our results show that an invariant QCOF model with a small descriptor dimensionality and cutoff outperforms all other models in most validation tasks, including scalability to large systems, force prediction in defective COFs, and phonon dispersion calculations. The best-performing QCOF model was then used to run large-scale simulations of thermal conductivity for defective CTF-1 and COF-LZU1 systems via non-equilibrium MD, revealing a more pronounced sensitivity of CTF-1 to structural defects. Stress-strain curves were also investigated, showing that the mechanical response remains nearly invariant at low defect densities, while asymmetric behaviour emerges at large strains. This work thus provides a foundation for the design of robust quantum-informed MLIP for large-scale property simulations of defective of extended network materials. |
| title | Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks |
| topic | Materials Science Chemical Physics I.6.4; J.2; J.6 |
| url | https://arxiv.org/abs/2604.21494 |