{"total":18,"rows":[{"id":"2502.03638","title":"SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models","created":"2025-02-05","cited_by_count":51,"area":null},{"id":"2403.15734","title":"Space Group Informed Transformer for Crystalline Materials Generation","created":"2024-03-23","cited_by_count":41,"area":null},{"id":"2401.17244","title":"LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation","created":"2024-01-30","cited_by_count":32,"area":null},{"id":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2512.04562","title":"LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models","created":"2025-12-04","cited_by_count":23,"area":null},{"id":"2406.07598","title":"Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency","created":"2024-06-11","cited_by_count":19,"area":null},{"id":"2410.17270","title":"MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks","created":"2024-10-07","cited_by_count":17,"area":null},{"id":"2502.02016","title":"A Periodic Bayesian Flow for Material Generation","created":"2025-02-04","cited_by_count":16,"area":null},{"id":"2406.10796","title":"Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data","created":"2024-06-16","cited_by_count":12,"area":null},{"id":"2503.00152","title":"Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation","created":"2025-02-28","cited_by_count":12,"area":null},{"id":"2505.16379","title":"Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey","created":"2025-05-22","cited_by_count":10,"area":null},{"id":"2406.12888","title":"A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction","created":"2024-06-03","cited_by_count":8,"area":null},{"id":"2411.04323","title":"Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks","created":"2024-11-06","cited_by_count":3,"area":null},{"id":"2505.20299","title":"MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery","created":"2025-05-08","cited_by_count":2,"area":null},{"id":"2506.15722","title":"UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2605.08960","title":"CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models","created":"2026-05-09","cited_by_count":2,"area":null},{"id":"2607.08996","title":"Model Agnostic Graph Prompt Learning for Crystal Property Prediction","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2609.26547","title":"Topology-Stratified Materials Discovery with A Flow-Based Generative Model","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}