Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks

TL;DR

This paper develops and benchmarks a machine learning interatomic potential for covalent organic frameworks, enabling large-scale, quantum-accurate simulations of their thermal and mechanical properties, especially in defective states.

Contribution

The authors introduce QCOF models based on MACE architecture, optimized for COFs, and demonstrate their superior accuracy, efficiency, and transferability for simulating defective COFs.

Findings

QCOF models outperform existing models in validation tasks.

Large-scale MD simulations reveal defect sensitivity in thermal conductivity.

Mechanical response remains stable at low defect densities, but varies at high strains.

Abstract

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.