Predicting Lattice Thermal Conductivity in 2D Materials: Integrating Experiments, Theory, and Machine Learning

ABSTRACT Lattice thermal conductivity is a critical parameter for assessing the thermal transport properties of materials. When confined to the monolayer limit, two‐dimensional materials display unique thermal characteristics distinct from their three‐dimensional counterparts. This article first provides a concise summary of three widely applied experimental methodologies—Raman thermometry, suspended microbridge techniques, and time‐domain thermoreflectance—and their utility in validating theoretical predictions. It subsequently delves into recent advancements in theoretical modeling, encompassing both equilibrium and nonequilibrium molecular dynamics studies; first‐principles calculations grounded in the phonon Boltzmann transport equation that account for higher‐order scattering phenomena such as four‐phonon processes and phonon–electron interactions; emerging methods based on normal mode analysis for detailed phonon contribution decomposition; and novel approaches employing the Wigner transport equation to unify the description of phonon coherence and wave‐like heat transport phenomena beyond conventional theoretical frameworks. In addition, the advent of machine learning has expanded the scope of direct thermal conductivity prediction and the development of high‐precision interatomic potentials, paving the way for high‐throughput screening and extensive simulations. This review contrasts the advantages and drawbacks of these methodologies, identifies key challenges facing the field, and sketches future directions for 2D thermal transport research, emphasizing the integration of multiscale modeling, data‐driven innovation, and the synergy between experiments and theoretical insights.