Machine learning potential-assisted design of thermoelectricvperformance in anharmonic CsPbBr3 and CsPbI3†

Abstract

The structural stability and thermoelectric properties of halide perovskites were investigated using a combination of machine learning approaches and density functional theory (DFT). A neuroevolution potential (NEP) was trained via a feedforward neural network and subsequently employed in molecular dynamics (MD) simulations to probe the phase stability of $\rm CsPbBr_3$ and $\rm CsPbI_3$. The simulations reveal a temperature-driven phase transition from the harmonic $\gamma$-phase to the anharmonic $\beta$- and $\alpha$-phases. Notably, the high-temperature $\alpha$-phase combines low thermal conductivity with structural stability, suggesting its promise as a thermoelectric material. \textcolor[rgb]{1.00,0.00,0.00}{The self-consistent phonon (SCPH) method addresses the limitations of the harmonic approximation by renormalizing phonon frequencies, thereby eliminating the unphysical imaginary modes.} Accurate electron-phonon interactions were critical for carrier transport analysis. Carrier mobility, calculated via the Boltzmann transport equation (BTE) with Wannier-interpolated electron-phonon coupling matrices, was found to be overestimated compared to experiments. This discrepancy is attributed to additional carrier scattering from dopants in n- or p-type semiconductors under operating conditions. At 600 K, the maximum predicted $ZT$ for $\alpha$-$\rm CsPbI_3$ is 1.59 under ideal conditions and 0.65 under realistic conditions, corresponding to thermoelectric conversion efficiencies of 10.0\% and 5.3\% in power generation devices.