Discovering new stable "mayenite-like" electrides using high-throughput computational screening

Electrides are a novel class of materials featuring electrons localized in pockets away from atomic nuclei. Due to their unique electronic localization, electrides exhibit exceptional properties, such as low work functions and high catalytic activity for NH₃ production. Since the discovery of the first inorganic electride, mayenite (Ca₁₂Al₁₄O₃₂:2e⁻), numerous electrides have been identified. However, mayenite remains uniquely stable: while most electrides degrade in air and moisture, mayenite is stable in ambient conditions, making it attractive for applications. This remarkable stability likely results from its synthesis method. Unlike typical electrides, which are produced directly via solid-state synthesis in controlled environments, mayenite is first synthesized as a stable, non-electride form (Ca₁₂Al₁₄O₃₃) and later reduced with a reagent (e.g., Ti) to selectively remove oxygen and form the electride. This "soft-chemistry" approach likely promotes air stability. The goal of this study is to use computational screening to discover new "mayenite-like" electrides that can be synthesized using soft-chemistry methods. We conduct high-throughput density functional theory (DFT) calculations on thousands of inorganic materials to identify those with off-nucleus electron states. We then assess whether anions can be selectively removed or cations added to stabilize these electride states. Machine learning-based atomic potentials help us evaluate ion mobility within the framework. From this screening, we identify several potential new electrides, which we further explore for their electronic structure, work function, and thermoionic emission properties. Beyond identifying new electrides, the generated data provide insights into the structural and chemical factors that favor electride formation.