  Designing polymers with high intrinsic thermal conductivity (TC) is
critically important for the thermal management of organic electronics and
photonics. However, this is a challenging task owing to the diversity of the
chemical space and the barriers to advanced synthetic
experiments/characterization techniques for polymers. In this Tutorial, the
fundamentals and implementation of combining classical molecular dynamics
simulation and machine learning (ML) for the development of polymers with high
TC are comprehensively introduced. We begin by describing the core components
of a universal ML framework, involving polymer datasets, property calculators,
feature engineering and informatics algorithms. Then, the process of
constructing interpretable regression algorithms for TC prediction is
introduced, aiming to extract the underlying relationships between
microstructures and TCs for polymers. We also explore the design of
sequence-ordered polymers with high TC using lightweight and mainstream active
learning algorithms. Lastly, we conclude by addressing the current limitations
and suggesting potential avenues for future research on this topic.
