Aqvolt: High-Fidelity Machine-Learned Force Fields for Halogenated Solid-State Electrolyte Materials

The escalating global demand for safer, high-energy density batteries underscores the need for advanced solid-state Li-ion technologies. Halide solid-state electrolyte (SSE) materials have recently emerged as a class of highly promising candidates that offer superior ionic mobility, wider electrochemical stability windows, and deformability for robust interfacial contact compared to their oxide and sulfide counterparts [1]. Despite this promise, closely studying these materials at larger scales has been a challenge. Prior screening approaches for SSEs have relied on holistic methods, obtaining conductivity values from the literature [2], creating machine learning models based on experimental data [3], or running Ab Initio Molecular Dynamics simulations on a few promising candidates [4], which are costly and too short to model diffusion on macroscopic time scales. In this work, we present AQVolt: a high-fidelity, off-equilibrium dataset for halogenated SSEs and machine-learned force fields (MLFFs) at the r2SCAN level of theory. Our approach begins with leveraging existing MLFFs [5] to perform machine learning Molecular Dynamics (MLMD) simulations and generating millions of diverse atomic configurations at various temperatures. A stratified sampling and dimensionality reduction methodology [6] is employed to reduce sampling redundancies by 98%. By performing single-point calculations from first-principles on these intelligently sampled structures, we significantly reduce our calculation workload while exploring diverse configurations. Benchmarking on current state-of-the-art foundational potentials suggests that limited exposure to off-equilibrium data can hinder their performance [5, 7-8]. AQVolt-trained MLFFs provide the unprecedented accuracy and speed necessary to conduct more accurate MD simulations, thereby predicting downstream properties pertinent to solid-state electrolytes such as ionic conductivity. AQVolt will accelerate the discovery of next-generation halide SSEs, paving the way for safer and higher-performance solid-state battery materials.