Discovering Stable Amorphous Ceramics: From Computational Prediction to Thin-Film Synthesis.

Area of Science:

• Materials Science
• Computational Materials Science

Background:

• Amorphous materials possess unique properties valuable for electronics, energy storage, and coatings.
• Discovering stable amorphous compositions is often reliant on serendipity or extensive experimentation.

Purpose of the Study:

• To develop a predictive computational framework for identifying stable amorphous material compositions.
• To demonstrate the efficacy of this workflow using yttrium tungsten nitride as a model system.

Main Methods:

• Utilizing computational random structure sampling to explore potential energy landscapes.
• Synthesizing predicted amorphous materials and characterizing their structural and functional properties.

Main Results:

• Identified yttrium tungsten nitride as a stable amorphous material using computational predictions.
• Experimental synthesis confirmed the material's amorphous nature with no detectable crystalline precipitates.
• Characterization revealed attractive properties, suggesting potential applications.

Conclusions:

• Computational random structure sampling is a robust method for discovering stable amorphous materials.
• The proposed theoretical-experimental workflow accelerates the discovery of amorphous ceramics for emerging technologies.