Deep learning reveals key predictors of thermal conductivity in covalent organic frameworks

Abstract

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 is not well understood. From a dataset of over 2,400 COFs, we find that conventional features like density, pore size, void fraction, and surface area do not reliably predict thermal conductivity. To overcome this, we train an attention-based machine learning model that accurately predicts thermal conductivities, even for structures outside the training set. We then use the attention mechanism to understand why the model works. Surprisingly, dangling molecular branches emerge as key predictors of thermal conductivity, alongside conventional geometric descriptors like density and pore size. Our findings show that COFs with dangling functional groups exhibit lower thermal transfer capabilities than otherwise. Molecular dynamics simulations confirm this, revealing significant mismatches in the vibrational density of states due to the presence of dangling branches.