Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials.

Area of Science:

• Materials Science
• Computational Chemistry
• Solid State Chemistry

Background:

• Ternary metal oxides are vital in numerous applications and are cataloged in experimental databases.
• However, the stability and structures of many potential ternary oxide compounds remain unexplored.
• Identifying new stable ternary oxides is crucial for technological advancement.

Purpose of the Study:

• To investigate unexplored chemical spaces of ternary metal oxides.
• To predict the lowest-energy crystal structures and stability of novel ternary oxide compounds.
• To discover new stable ternary oxide systems using computational methods.

Main Methods:

• Employed extensive crystal structure prediction methods.
• Utilized machine-learned potentials to accelerate simulations.
• Examined 181 ternary metal oxide systems, determining lowest-energy structures and representative stoichiometry.

Main Results:

• Discovered 45 ternary oxide systems with stable compounds.
• The majority of newly discovered stable oxides incorporate noble metals.
• Compared findings with other theoretical databases, highlighting strengths and limitations of informatics-based searches.

Conclusions:

• Heuristic-based structure searches, accelerated by machine learning, are effective for discovering novel materials.
• This approach requires modest computational resources, making it a promising avenue for future materials discovery.
• The identified stable ternary oxides, particularly those with noble metals, warrant further experimental investigation.