Interpretable Machine Learning Reveals Design Rules for Thermal‐Mechanical Decoupling in Covalent Organic Frameworks

Thermal management materials face a persistent challenge: high thermal conductivity ( κ ) and low Young's modulus (E) are often mutually exclusive. Stiff materials typically conduct heat well while compliant materials tend to thermally insulate. Covalent organic frameworks (COFs) offer exceptional structural tunability that breaks this trade‐off, yet a systematic understanding of κ ‐E decoupling remains elusive. Here we establish an interpretable machine learning framework integrating molecular simulations, six benchmarked models, and SHAP‐based feature analysis to screen over 52 000 topologically valid three‐dimensional COFs. Using random forest as the optimal model, we identify 26 COFs that simultaneously achieve high κ (> 0.8 W m − 1 K − 1 ) and low E (< 8 GPa), a combination difficult to attain by conventional design. SHAP analysis reveals that decoupling arises from nonlinear cooperative actions of multiple descriptors rather than single‐feature tuning. Phonon‐level transport analyses show that intermediate density, strong covalent connectivity, moderate porosity, and low linker fragment complexity balance volumetric heat capacity, phonon group velocity, and phonon lifetime, enabling high κ while preserving low E. This work provides a blueprint for thermally conductive yet mechanically compliant COFs and establishes a generalizable workflow for exploring property decoupling in framework materials.