  Polymers, integral to advancements in high-tech fields, necessitate the study
of their thermal conductivity (TC) to enhance material attributes and energy
efficiency. The TC of polymers obtained by molecular dynamics (MD) calculations
and experimental measurements is slow, and it is difficult to screen polymers
with specific TC in a wide range. Existing machine learning (ML) techniques for
determining polymer TC suffer from the problems of too large feature space and
cannot guarantee very high accuracy. In this work, we leverage TCs from
accessible datasets to decode the Simplified Molecular Input Line Entry System
(SMILES) of polymers into ten features of distinct physical significance. A
novel evaluation model for polymer TC is formulated, employing four ML
strategies. The Gradient Boosting Decision Tree (GBDT)-based model, a focal
point of our design, achieved a prediction accuracy of R$^2$=0.88 on a dataset
containing 400 polymers. Furthermore, we used an interpretable ML approach to
discover the significant contribution of quantitative estimate of drug-likeness
and number of rotatable bonds features to TC, and analyzed the physical
mechanisms involved. The ML method we developed provides a new idea for
physical modeling of polymers, which is expected to be generalized and applied
widely in constructing polymers with specific TCs and predicting all other
properties of polymers.
