Accelerating Discovery of Ternary Chiral Materials via Large-Scale Random Crystal Structure Prediction

Area of Science:

• Materials Science
• Condensed Matter Physics
• Crystallography

Background:

• Chiral inorganic crystals, especially semiconductors and semimetals with Weyl points, are of significant interest for advanced electronic applications.
• Existing materials databases lack a diverse range of these chiral materials, hindering further research and development.

Purpose of the Study:

• To develop and implement a scalable computational strategy for predicting novel chiral inorganic crystals.
• To identify new materials exhibiting unique topological properties, nonlinear optical responses, and superconductivity.

Main Methods:

• Combined universal machine-learning interatomic potentials (uMLIPs) for high-throughput structure optimization with random structure search (RSS).
• Performed large-scale crystal structure prediction in ternary systems with variable compositions.
• Screened for chiral space groups and validated promising candidates using first-principles calculations.

Main Results:

• Identified over 20 million randomly generated chiral structures, with numerous potentially stable phases through uMLIP optimization.
• Confirmed more than 260 novel chiral inorganic crystals via first-principles validation.
• Observed quantum phenomena in some candidates, including nonlinear Hall effect, quantum metric, and topological points.

Conclusions:

• Substantially expanded the library of candidate chiral functional materials.
• Demonstrated a scalable and effective strategy for predicting ternary chiral material systems.
• Highlighted the potential of these newly discovered crystals for applications in topological electronics, nonlinear optics, and superconductivity.