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

Double perovskites (ABC₂D₆) 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 (<0.1 Wm^-1K^-1). Remarkably, Cs₂HgPtCl₆ was found to possess a bandgap of 0.35 eV and an LTC of 0.071 Wm^-1K^-1 at room temperature-the lowest ever reported for isotropic bulk materials, comparable to air. The result was independently confirmed by molecular dynamics simulations with a DeePMD potential and phonon lifetime extraction using DynaPhoPy. This work establishes an efficient machine learning-assisted framework for fast screening of dynamic stability and accurate prediction of phonon transport in complex materials, highlighting double perovskites as Double Perovskites, High-Throughput DFT, Machine Learning, Phonon Boltzmann Transport, Record-Low Lattice Thermal Conductivity promising candidates for thermoelectric and thermal insulation applications.