Data-driven Discovery of Inorganic Electrides for Energy Applications

Dr. Qiang Zhu, Assistant Professor

Department of Physics and Astronomy

University of Nevada Las Vegas

Las Vegas, NV  89154

Electrides represent a unique class of materials where excess electrons trapped inside crystal cavities behave as anions. The trapped electrons are loosely bound near the Fermi energy level and can be used to design new materials with low work functions or minimum thermodynamic work to remove electrons from the solid, high electron mobility, and nontrivial band topology. However, despite the rapidly growing interest in electrides by physicists, chemists, and materials scientists, electride research has been hindered due to a lack of candidate materials. This research aims to accelerate the discovery of electrides through developing an advanced materials screening method that combines group theory, crystal structure prediction, machine learning, and high-throughput screening. Specific objectives of this research include: (1) incorporating symmetry relations into materials structure screening, (2) developing physics-informed machine learning models that can perform quick evaluations of materials’ structural and electronic properties; and 3) constructing an electride database by screening promising material structures within a large chemical space. The simulation results and database will provide the materials science community with a large number of potential electrides, allowing the experimental community to test these predictions and probe potential technological applications. These computational approaches will be transferable across all classes of inorganic materials and may be utilized for a wide range of energy research activities.

This research was selected for funding by the Office of Basic Energy Sciences and

the DOE Established Program to Stimulate Competitive Research.