Selective Random Structure Search (SRSS): Unbiased Exploration of Polymorphs in Crystals

Abstract

Crystal structure prediction has traditionally relied on prototype-based seeding, approaches that often bias sampling toward known low-energy basins and overlook metastable polymorphs with unconventional symmetries. Here, we introduce Selective Random Structure Search (SRSS), a high-throughput, unbiased framework designed to explore the configurational space of crystalline materials across all dimensions. SRSS combines symmetry-constrained random generation with feature-based diversity selection and rapid relaxation and stability evaluation via universal machine-learning interatomic potentials (uMLIPs). Applied to diverse systems, including bulk system SiC and BaPtAs, 2D layered compounds NbSe2, and 1D nanotubes GaN, SRSS successfully recovers known ground states while revealing numerous previously unreported, dynamically stable polymorphs. Notable discoveries include complex cage-like SiC polytypes, low-energy BaPtAs polymorphs beyond experimental records, a semiconducting orthorhombic phase of 2D-NbSe2, and distinct armchair/zigzag GaN nanotubes. Crucially, the entire workflow operates efficiently on standard CPU resources without GPU acceleration, demonstrating that rigorous, hypothesis-free polymorph discovery is accessible even in resource-limited settings. SRSS thus establishes a robust, scalable platform for mapping the full landscape of crystal stability, bridging the gap between exhaustive search and computational feasibility.