Best Practices for Modelling Electrides

TL;DR

This paper evaluates the effectiveness of various computational methods for modeling electrides, highlighting that standard approaches reliably capture key properties and supporting efficient high-throughput discovery of these materials.

Contribution

It demonstrates that common exchange-correlation functionals are effective for electride modeling, enabling reliable and efficient high-throughput screening strategies.

Findings

Standard methods capture electride character reliably

Higher-cost approaches do not always outperform simpler methods

Supports tiered computational strategies for electride discovery

Abstract

Materials in which electrons occupy interstitial sites as anions are called electrides and exhibit unusual dimensionality-dependent electronic behavior. These properties make electrides attractive for catalysis, transparent conductors, and emergent quantum phenomena, yet their theoretical treatment remains challenging. In conventional materials, the ground-state atomic structure dictates the electronic configuration, whereas in electrides the electronic structure can instead govern the atomic arrangement. Here, the performance of commonly used exchange-correlation functionals is evaluated for representative one-, two-, and three-dimensional electrides. The results show that higher-cost approaches do not necessarily perform better across all cases, while standard methods capture the qualitative electride character and many key energetic and structural trends with surprising reliability. This behavior, likely arising from fortuitous error cancellation, supports the reliability of legacy studies in the field and the viability of efficient high-throughput exploration using low-cost methods. Overall, the findings support a tiered computational strategy for electride modelling, integrating system-specific heuristics with efficient first-principles screening. This approach balances computational feasibility with physical fidelity and underscores the continuing leadership of theory in the predictive discovery of electride materials across dimensionalities.