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Accelerating the discovery of disordered multi-component solid-state electrolytes using machine learning interatomic potentials
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DOI：10.1039/D5TA05321H.png)
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Abstract
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Machine learning interatomic potentials (MLIPs) are rapidly emerging as powerful tools for materials simulations; offering a promising pathway to explore complex systems beyond the reach of traditional methods. This study investigates the application of MLIPs focused on the MACE architecture to multi-component; disordered solid-state electrolytes (SSEs); a critical class of materials for the next-generation solid-state batteries. We first benchmark the performance of MACE against established SSE families; Na1+xZr2SixP3-xO12 and Li4xGexP1-xO4-4xS4x; confirming their general applicability while identifying key considerations for robust potential development in chemically diverse systems. This workflow; emphasizing the selection of representative configurations; provides critical insights for constructing reliable models in complex; multi-components environments. We further demonstrate the predictive power of this approach by constructing a high-performance MLIP for the novel halide system Li3InxY1-xBr6yCl6-6y (x; y ∈ [0; 1] and x + y ≥ 1); leading to the identification of the Li3In0.5Y0.5Br3Cl3 stoichiometry with the most favorable predicted ion transport properties. By analyzing the MD trajectories generated in this work using our MLIP; we identified two distinct Li-ion migration pathways in this material. The trained model facilitates the computational investigation of intricate mixed cation/anion substitutions in halide SSEs; offering new insights into higher-entropy systems incorporating multi-components. Our results underscore the capability of MLIPs to accelerate the discovery cycle of complex functional materials and provide a robust computational framework for designing advanced SSEs.
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