{"total":42,"rows":[{"id":"2110.06197","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","created":"2021-10-12","cited_by_count":270,"area":null},{"id":"2306.00984","title":"StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners","created":"2023-06-01","cited_by_count":177,"area":null},{"id":"2312.04567","title":"Scaling Laws of Synthetic Images for Model Training ... for Now","created":"2023-12-07","cited_by_count":108,"area":null},{"id":"2106.05258","title":"Generative Models as a Data Source for Multiview Representation Learning","created":"2021-06-09","cited_by_count":104,"area":null},{"id":"2311.09235","title":"Scalable Diffusion for Materials Generation","created":"2023-10-18","cited_by_count":54,"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":"2307.02707","title":"Towards Symmetry-Aware Generation of Periodic Materials","created":"2023-07-06","cited_by_count":26,"area":null},{"id":"2210.11931","title":"Deep Reinforcement Learning for Inverse Inorganic Materials Design","created":"2022-10-21","cited_by_count":19,"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":"2401.03862","title":"End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction","created":"2024-01-08","cited_by_count":10,"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":"2204.11953","title":"Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials","created":"2022-04-25","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":"2305.05247","title":"Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy","created":"2023-05-09","cited_by_count":7,"area":null},{"id":"2602.20210","title":"Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling","created":"2026-02-23","cited_by_count":7,"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":"2202.13309","title":"Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes","created":"2022-02-27","cited_by_count":5,"area":null},{"id":"2601.12554","title":"Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie","created":"2026-01-18","cited_by_count":5,"area":null},{"id":"2309.06710","title":"Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm","created":"2023-09-13","cited_by_count":4,"area":null},{"id":"2503.03963","title":"Generative Learning of Densities on Manifolds","created":"2025-03-05","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":"2101.04383","title":"Interpretable discovery of new semiconductors with machine learning","created":"2021-01-12","cited_by_count":3,"area":null},{"id":"2112.09570","title":"A Binded VAE for Inorganic Material Generation","created":"2021-12-17","cited_by_count":3,"area":null},{"id":"2212.12047","title":"Deep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterials","created":"2022-12-22","cited_by_count":3,"area":null},{"id":"2310.00475","title":"Generative Design of inorganic compounds using deep diffusion language models","created":"2023-09-30","cited_by_count":2,"area":null},{"id":"2406.12368","title":"MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations","created":"2024-06-18","cited_by_count":2,"area":null},{"id":"2103.00608","title":"Active learning based generative design for the discovery of wide bandgap materials","created":"2021-02-28","cited_by_count":1,"area":null},{"id":"2112.03528","title":"Physics guided deep learning generative models for crystal materials discovery","created":"2021-12-07","cited_by_count":1,"area":null},{"id":"2206.13578","title":"Materials Transformers Language Models for Generative Materials Design: a benchmark study","created":"2022-06-27","cited_by_count":1,"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":"2211.15895","title":"Composition based oxidation state prediction of materials using deep learning","created":"2022-11-29","cited_by_count":1,"area":null},{"id":"2305.15920","title":"Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2504.10201","title":"VibrantLeaves: A principled parametric image generator for training deep restoration models","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2104.03935","title":"OGGN: A Novel Generalized Oracle Guided Generative Architecture for Modelling Inverse Function of Artificial Neural Networks","created":"2021-04-08","cited_by_count":0,"area":null},{"id":"2305.18978","title":"IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics","created":"2023-05-30","cited_by_count":0,"area":null},{"id":"2307.13581","title":"Comparing Forward and Inverse Design Paradigms: A Case Study on Refractory High-Entropy Alloys","created":"2023-07-25","cited_by_count":0,"area":null},{"id":"2506.02254","title":"Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps","created":"2025-06-02","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},{"id":"2605.30743","title":"A Padding Method for Enhanced Encoding of Inorganic Structures with Varying Chemical Compositions","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2609.33407","title":"Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}