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Aixian She

Publications and source records attributed to Aixian She.

2 recordsLinked to original sources
arXiv2026

GEWUM: General Exploration Workflow for the Utopia of Materials: A Unified Platform for Automated Structure Generation, Selection, and Validation

The fragmented landscape of existing computational tools often hinders the seamless integration of large-scale structure prediction with rigorous stability validation. To address this, we present GEWUM (General Exploration Workflow for the Utopia of Materials), an open-source platform that integrates the Selective Random Structure Search (SRSS) strategy with universal Machine Learning Interatomic Potentials (uMLIPs) to automate and accelerate materials discovery. With native support for SLURM-based HPC clusters, GEWUM unifies the entire workflow from structure generation and diversity-preserving selection to thermodynamic/dynamic stability assessments and property calculations. The platform further incorporates built-in visualization tools, including Sankey diagrams for space-group evolution, violin plots for energy distributions, and t-SNE/UMAP embeddings for structural diversity, enabling intuitive interpretation of screening results. We demonstrate GEWUM through three case studies: low-energy polymorph prediction in Al-Sc-N, identification of a P-62c phase of U3Si5, and high-pressure structure prediction of ThH10 at 150 GPa. Benchmark tests confirm reasonable agreement in thermophysical property predictions.

cond-mat.mtrl-sci↗
arXiv2026

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

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.

cond-mat.mtrl-sci↗