{"total":29,"rows":[{"id":"2312.03687","title":"MatterGen: a generative model for inorganic materials design","created":"2023-12-06","cited_by_count":98,"area":null},{"id":"2204.00056","title":"SELFIES and the future of molecular string representations","created":"2022-03-31","cited_by_count":76,"area":null},{"id":"2402.03992","title":"Space Group Constrained Crystal Generation","created":"2024-02-06","cited_by_count":73,"area":null},{"id":"2312.07511","title":"A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems","created":"2023-12-12","cited_by_count":68,"area":null},{"id":"2311.09235","title":"Scalable Diffusion for Materials Generation","created":"2023-10-18","cited_by_count":54,"area":null},{"id":"2502.03638","title":"SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models","created":"2025-02-05","cited_by_count":51,"area":null},{"id":"2310.04925","title":"Crystal-GFN: sampling crystals with desirable properties and constraints","created":"2023-10-07","cited_by_count":31,"area":null},{"id":"2203.14352","title":"Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints","created":"2022-03-27","cited_by_count":30,"area":null},{"id":"2109.12283","title":"Scalable deeper graph neural networks for high-performance materials property prediction","created":"2021-09-25","cited_by_count":26,"area":null},{"id":"2502.02016","title":"A Periodic Bayesian Flow for Material Generation","created":"2025-02-04","cited_by_count":16,"area":null},{"id":"2110.01442","title":"A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing","created":"2021-10-01","cited_by_count":13,"area":null},{"id":"2501.03278","title":"DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules","created":"2025-01-05","cited_by_count":13,"area":null},{"id":"2302.00485","title":"Equivariant Message Passing Neural Network for Crystal Material Discovery","created":"2023-02-01","cited_by_count":10,"area":null},{"id":"2411.09429","title":"AI-driven inverse design of materials: Past, present and future","created":"2024-11-14","cited_by_count":10,"area":null},{"id":"2501.02144","title":"Establishing baselines for generative discovery of inorganic crystals","created":"2025-01-04","cited_by_count":9,"area":null},{"id":"2105.05278","title":"Polygrammar: Grammar for Digital Polymer Representation and Generation","created":"2021-05-05","cited_by_count":8,"area":null},{"id":"2503.10471","title":"Siamese Foundation Models for Crystal Structure Prediction","created":"2025-03-13","cited_by_count":8,"area":null},{"id":"2508.03278","title":"Artificial Intelligence and Generative Models for Materials Discovery -- A Review","created":"2025-08-05","cited_by_count":8,"area":null},{"id":"2109.04007","title":"MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art","created":"2021-09-09","cited_by_count":5,"area":null},{"id":"2304.05498","title":"GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2511.22652","title":"Generative Models for Crystalline Materials","created":"2025-11-27","cited_by_count":4,"area":null},{"id":"2306.04510","title":"Unified Model for Crystalline Material Generation","created":"2023-06-07","cited_by_count":3,"area":null},{"id":"2310.10695","title":"Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells","created":"2023-10-16","cited_by_count":2,"area":null},{"id":"2209.11234","title":"Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering","created":"2022-09-15","cited_by_count":1,"area":null},{"id":"2411.08464","title":"A Generation Framework with Strict Constraints for Crystal Materials Design","created":"2024-11-13","cited_by_count":1,"area":null},{"id":"2601.22009","title":"MEIDNet: Multimodal generative AI framework for inverse materials design","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2406.14840","title":"Automated architectural space layout planning using a physics-inspired generative design framework","created":"2024-06-21","cited_by_count":0,"area":null},{"id":"2510.05494","title":"Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2604.11827","title":"Inverse Design of Inorganic Compounds with Generative AI","created":"2026-04-11","cited_by_count":0,"area":null}],"next_cursor":null}