Wei-Fan Huang | Searcharxiv

arXiv2026

Strain-Dependent Ionic Transport in Li3YCl6 Solid Electrolytes

Solid-state batteries require electrolytes that sustain high ionic conductivity under the mechanical environment of a functioning cell. Lattice strain, arising from stack pressure, thermal cycling, or lattice mismatch at interfaces, can either enhance or suppress Li+ transport in solid electrolytes, yet how it couples to the underlying diffusion mechanism remains poorly understood. Using Li3YCl6 halide superionic conductor, we address this with large-scale molecular dynamics simulations driven by an Atomic Cluster Expansion (ACE) machine learning interatomic potential trained on first-principles data. The ACE model faithfully reproduces experimental and \textit{ab initio} structural, mechanical, and transport properties of Li3YCl6. We find that Li+ diffusion in Li3YCl6 follows a two-regime Arrhenius behavior, crossing over at a critical temperature $T_c$ from one-dimensional hopping at low temperature to three-dimensional cooperative diffusion at high temperature. Strain substantially modulates diffusivity: tensile strain enhances it while compressive strain suppresses it, yet leaves $T_c$ invariant, indicating that strain tunes diffusion efficiency without reshaping the underlying transport framework. In each regime, the mechanistic origin differs: altered activation barriers dominate at low temperature, while modified pre-exponential factors become critical at high temperature. These results establish lattice strain as a design lever for ionic conductivity in Li3YCl6 solid-state electrolytes.

cond-mat.mtrl-sci↗

arXiv2025

Cartesian atomic moment machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency. While leading MLIPs rely on representing atomic environments using spherical tensors, Cartesian representations offer potential advantages in simplicity and efficiency. Here, we introduce the Cartesian Atomic Moment Potential (CAMP), an approach to building MLIPs entirely in Cartesian space. CAMP constructs atomic moment tensors from neighboring atoms and employs tensor products to incorporate higher body-order interactions, providing a complete description of local atomic environments. Integrated into a graph neural network (GNN) framework, CAMP enables physically motivated, systematically improvable potentials. The model demonstrates excellent performance across diverse systems, including periodic structures, small organic molecules, and two-dimensional materials, achieving accuracy, efficiency, and stability in molecular dynamics simulations that rival or surpass current leading models. CAMP provides a powerful tool for atomistic simulations to accelerate materials understanding and discovery.

cond-mat.mtrl-sci↗