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Emerging Developments of Inorganic Magnesium Solid-State Electrolytes

Anik Brinckerhoff1 and Hong Fang1,2,3,4,*

  • *Contact author: hong.fang@rutgers.edu

PRX Energy 5, 027001 – Published 16 April, 2026

DOI: https://doi.org/10.1103/2df5-tywt

Abstract

Usable magnesium batteries are promising alternatives to lithium technology. The currently most widely used battery type based on lithium is reaching its cost-reduction limit due to the relative rarity of the precursors and environmental side effects. While other ions, such as sodium, have also been investigated for use in batteries, magnesium stands out due to its several advantages, including high abundance and high theoretical capacity. While most of the current magnesium batteries contain liquid electrolytes, solid-state electrolytes (SSEs) are especially of interest due to their potential advantages over the liquid ones, including enhanced safety, higher energy density, wider electrochemical stability window and operative temperature range, resistance to side reactions and degradation, and simplified battery-pack design. However, Mg-based SSEs have their own difficulties, most notably the lack of ionic conductivity. Only a few Mg-ion conductors can achieve superionic conductivity at room temperature with a low activation energy. Thus, researchers continually search for inorganic solids that can conduct Mg ions better while having no other issues, such as high electronic conductivity or electrochemical instability. With a wide range of materials that have already been investigated and a vast array with potential that remains untapped, it is essential to keep updated on this rapidly expanding field. This minireview aims to identify emerging Mg-ion conductors that can serve as promising candidates for SSEs, as well as to review methods for enhancing the relevant properties of established materials. We highlight the new materials and methods investigated in the last 2–3 years that are yet to be covered in a review. An exciting new area of emerging research is highlighted, utilizing machine learning and high-throughput materials screening and calculations to identify promising inorganic Mg-ion conductors from materials databases.

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