Computationally assisted identification of functional inorganic materials

Area of Science:

• Inorganic Chemistry
• Materials Science
• Computational Materials Design

Background:

• Designing complex inorganic materials with diverse structures is challenging.
• Functional material properties are often linked to the chemistry of their constituent modules.
• Computational methods can aid in identifying novel material structures.

Purpose of the Study:

• To present a computational method for identifying complex inorganic materials with multiple atom types and geometries.
• To demonstrate the method's capability in discovering new functional materials.
• To validate the method by identifying a potential solid oxide fuel-cell cathode material.

Main Methods:

• Assembling candidate structures from extended modules with chemically realistic atomic environments.
• Ranking candidate structures based on their chemical and structural properties.
• Combining computational identification with experimental validation.

Main Results:

• A novel complex oxide, Y(2.24)Ba(2.28)Ca(3.48)Fe(7.44)Cu(0.56)O21, was computationally identified and experimentally isolated.
• The identified material possesses a large unit cell (over 60 angstroms) with 148 atoms.
• The material exhibits properties suitable for solid oxide fuel-cell cathode applications.

Conclusions:

• The presented computational method is effective for designing complex inorganic materials.
• This approach facilitates the discovery of novel materials with specific functionalities.
• The identified oxide demonstrates potential for advanced energy applications, such as solid oxide fuel cells.