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

Chiral inorganic crystals, particularly semiconductors with Weyl points near the band edges or semimetals hosting Weyl points at the Fermi level, have attracted considerable interest; yet, they remain scarce in existing materials databases. This study presents a prediction pathway by combining universal machine-learning interatomic potentials (uMLIPs) for high-throughput structure optimization with the broad exploration capability of random structure search (RSS), enabling large-scale crystal structure prediction in ternary systems with variable compositions, followed by targeted screening for chiral space groups. Through uMLIP-based high-throughput optimization and stability assessment, a large number of potentially stable phases were identified from over 20 million randomly generated chiral structures. First-principles validation further confirmed more than 260 chiral inorganic crystals with potential applications in topological properties, nonlinear optics, and superconductivity. Some of these materials exhibit notable quantum phenomena, such as the nonlinear Hall effect driven by Berry curvature dipole, quantum metric, and symmetry-protected 6-fold degenerate topological points, long Fermi arcs, and large magnetoresistance. This work substantially expands the pool of candidate chiral functional materials and offers a scalable strategy for predicting ternary material systems.