Mechanistic study of mixed lithium halides solid-state electrolytes
Phys. Rev. Materials 10, 085403 – Published 26 August, 2026
DOI: https://doi.org/10.1103/czy4-7lfp
Abstract
Lithium halides with the general formula , where indicates metal ions and indicates halide anions, are very actively studied as solid-state electrolytes, because of relatively low cost, high stability, and Li conductivity. The structure and properties of these halide-based solid electrolytes can be tuned by alloying, e.g., using different halides and/or transition metals simultaneously. The large chemical space is difficult to sample by experiments, making simulations based on broadly applicable machine-learning interatomic potentials (MLIPs) a promising approach to elucidate structure-property relations, and facilitate the design of better-performing compositions. Here, we focus on the system, for which reliable experimental data exists, and use the recently -developed universal MLIP potential, called PET-MAD, which is based on the Point Edge Transformer (PET) architecture trained on the Massive Atomic Diversity (MAD) dataset to investigate the structure of the alloy, the interplay of crystalline lattice, volume and chemical composition, and its effect on Li conductivity. We find that the distribution of Cl and Br atoms is only weakly correlated, and that the primary effect of alloying is to modulate the lattice parameter—although it can also trigger transition between different lattice symmetries. By comparing constant-volume and constant-pressure simulations, we disentangle the effect of lattice parameter and chemical composition on the conductivity, finding that the two effects compensate each other, reducing the overall dependency of conductivity on alloy composition. We also study the effect of Y-In metal substitution, finding a small increase in the conductivity for the phase at 25% In content, and an overall higher conductivity for the phase. An extended study of the effect of metal substitution, enabled by the semiquantitative accuracy of the universal model, shows that the effect of metal-site alloying is small, indicating that alloying can be used to optimize cost, or electrochemical stability, without major impact on bulk conductivity.
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Machine Learning for Materials Discovery and Understanding
The Editors of Physical Review Materials are pleased to present the Collection on Machine Learning for Materials Discovery and Understanding, highlighting cutting-edge advances in machine learning method development and applications for materials discovery and fundamental understanding of the structure-property-function relationship. The Collection is being guest-edited by Deyu Lu of Brookhaven National Laboratory (USA) and Jinlan Wang of Southeast University (China). Every article published in this collection underwent a rigorous peer review process, adhering to the same high standards applied to all papers. The Physical Review Materials editorial team managed the peer review and made all editorial decisions.
