Machine Learning Discovery of Record‐Low Lattice Thermal Conductivity in Double Perovskites

ABSTRACT Double perovskites (ABC 2 D 6 ) are versatile materials with applications in photovoltaics, optoelectronics, and thermoelectrics, where phonon‐mediated thermal transport is critical. However, high‐throughput phonon calculations by density functional theory (DFT) are computationally prohibitive due to the large supercells required. We develop a deep learning interatomic potential, Elemental‐SDNNFF, trained directly on DFT‐calculated forces within an active learning framework, enabling efficient prediction of phonon properties across thousands of double perovskites. Using this model, we screened 9709 cubic double perovskite structures, identifying 1597 dynamically stable candidates. Their lattice thermal conductivities (LTCs) were predicted by coupling Elemental‐SDNNFF with the Boltzmann Transport Equation, including off‐diagonal contributions. For the most promising compounds, DFT validation and four‐phonon scattering calculations revealed ultralow LTCs (