Layered perovskite lithium yttrium titanate ( LiYTiO 4 ) has recently emerged as a promising low-potential, ultrahigh-rate anode material for lithium-ion batteries. However, its lattice dynamics and thermal transport behavior remain largely unexplored, limiting a complete assessment of its practical applicability. Here, we combine experimental thermal conductivity measurements with a systematic theoretical study based on a machine-learning neural evolution potential (NEP). Our computational framework integrates the temperature-dependent effective potential method with the Wigner thermal transport (WTT) formalism, explicitly accounting for both diagonal and off-diagonal components of the heat-flux operator. Phonon calculations at 0 K reveal dynamical instabilities arising from rotations of the TiO 6 octahedra, which are stabilized at finite temperatures through anharmonic phonon renormalization. By incorporating particlelike phonon propagation and wavelike phonon tunneling within the WTT framework, we predict an orientation-averaged room-temperature lattice thermal conductivity ( κ L ) of 3.6 Wm − 1 K − 1 , which is qualitatively consistent with the experimentally measured value of 3.2 ± 0.08 Wm − 1 K − 1 for polycrystalline samples. To further elucidate the role of ionic motion at elevated temperatures, we compute κ L using the Green-Kubo approach within equilibrium molecular dynamics simulations based on the trained NEP, yielding improved agreement with experiment relative to the WTT prediction. Our results identify intrinsically low thermal conductivity as a key limitation for the practical application of LiYTiO 4 and provide broader insight into anharmonic lattice dynamics and thermal transport in complex battery materials.
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Anonymous et al. (2026) studied this question.
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