The thermal conductivity of covalent organic frameworks (COFs), an emerging class of nanoporous polymeric materials, is crucial for many applications, yet the link between their structure and thermal properties remains poorly understood. Analysis of a dataset containing over 2,400 COFs reveals that conventional features such as density, pore size, void fraction, and surface area do not reliably predict thermal conductivity. To address this, an attention-based machine learning model was trained, accurately predicting thermal conductivities even for structures outside the training set. The attention mechanism was then utilized to investigate the model's success. The analysis identified dangling molecular branches as a key predictor of thermal conductivity, leading us to define the dangling mass ratio (DMR), a descriptor that quantifies the fraction of atomic mass in dangling branches relative to the total COF mass. Feature importance assessments on regression models confirm the significance of DMR in predicting thermal conductivity. These findings indicate that COFs with dangling functional groups exhibit lower thermal transfer capabilities. Molecular dynamics simulations support this observation, revealing significant mismatches in the vibrational density of states due to the presence of dangling branches.

翻译：共价有机框架（COFs）作为一类新兴的纳米多孔聚合物材料，其热导率在许多应用中至关重要，然而其结构与热学性质之间的关联仍不甚明确。通过对包含2400余种COFs的数据集进行分析，我们发现密度、孔径、孔隙率和表面积等传统特征无法可靠预测热导率。为此，我们训练了一个基于注意力机制的机器学习模型，该模型即使对训练集之外的结构也能准确预测热导率。随后利用注意力机制探究了模型成功的原因。分析发现悬垂分子支链是预测热导率的关键因素，据此我们定义了悬垂质量比（DMR）——该描述符量化了悬垂支链中原子质量相对于COF总质量的占比。基于回归模型的特征重要性评估证实了DMR在预测热导率中的显著性。这些发现表明，具有悬垂官能团的COFs表现出较低的热传导能力。分子动力学模拟支持了这一观测，揭示了悬垂支链的存在会导致振动态密度出现显著失配。