Date of Award
Fall 2025
Document Type
Open Access Thesis
Department
Mechanical Engineering
First Advisor
Ming Hu
Abstract
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 9,709 cubic double perovskite structures, identifying 1,597 dynamically stable candidates. Their lattice thermal conductivities (LTCs) were predicted by coupling Elemental-SDNNFF with the Boltzmann Transport Equation, including off-diagonal (diffuson) 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 promising candidates for thermoelectric and thermal insulation applications.
Rights
© 2025, Md Zaibul Anam
Recommended Citation
Anam, M. Z.(2025). Data-Driven Discovery of Double Perovskites With Exceptionally Low Lattice Thermal Conductivity. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/8698