{"total":54,"rows":[{"id":"2406.04713","title":"FlowMM: Generating Materials with Riemannian Flow Matching","created":"2024-06-07","cited_by_count":109,"area":null},{"id":"2312.03687","title":"MatterGen: a generative model for inorganic materials design","created":"2023-12-06","cited_by_count":98,"area":null},{"id":"2402.04379","title":"Fine-Tuned Language Models Generate Stable Inorganic Materials as Text","created":"2024-02-06","cited_by_count":93,"area":null},{"id":"2503.03965","title":"All-atom Diffusion Transformers: Unified generative modelling of molecules and materials","created":"2025-03-05","cited_by_count":75,"area":"diffusion"},{"id":"2405.16852","title":"EM Distillation for One-step Diffusion Models","created":"2024-05-27","cited_by_count":62,"area":"diffusion"},{"id":"2410.23405","title":"FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions","created":"2024-10-30","cited_by_count":53,"area":"diffusion"},{"id":"2502.03638","title":"SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models","created":"2025-02-05","cited_by_count":51,"area":null},{"id":"2405.15986","title":"Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity","created":"2024-05-24","cited_by_count":42,"area":"diffusion"},{"id":"2406.00633","title":"Improving GFlowNets for Text-to-Image Diffusion Alignment","created":"2024-06-02","cited_by_count":27,"area":"diffusion"},{"id":"2503.09814","title":"A practical guide to machine learning interatomic potentials -- Status and future","created":"2025-03-12","cited_by_count":26,"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":"2410.21317","title":"MatExpert: Decomposing Materials Discovery by Mimicking Human Experts","created":"2024-10-26","cited_by_count":18,"area":null},{"id":"2401.08032","title":"Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study","created":"2024-01-16","cited_by_count":17,"area":null},{"id":"2405.12221","title":"Images that Sound: Composing Images and Sounds on a Single Canvas","created":"2024-05-20","cited_by_count":17,"area":"diffusion"},{"id":"2502.02016","title":"A Periodic Bayesian Flow for Material Generation","created":"2025-02-04","cited_by_count":16,"area":null},{"id":"2503.00522","title":"Periodic Materials Generation using Text-Guided Joint Diffusion Model","created":"2025-03-01","cited_by_count":15,"area":"diffusion"},{"id":"2505.10994","title":"Space Group Equivariant Crystal Diffusion","created":"2025-05-16","cited_by_count":15,"area":null},{"id":"2409.06762","title":"Generative Hierarchical Materials Search","created":"2024-09-10","cited_by_count":13,"area":null},{"id":"2510.23040","title":"LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation","created":"2025-10-27","cited_by_count":13,"area":"diffusion"},{"id":"2406.10796","title":"Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data","created":"2024-06-16","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":"2604.02270","title":"Crystalite: A Lightweight Transformer for Efficient Crystal Modeling","created":"2026-04-02","cited_by_count":10,"area":null},{"id":"2410.01464","title":"Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials","created":"2024-10-02","cited_by_count":9,"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":"2502.03146","title":"Symmetry-Aware Bayesian Flow Networks for Crystal Generation","created":"2025-02-05","cited_by_count":9,"area":null},{"id":"2507.03602","title":"Kinetic Langevin Diffusion for Crystalline Materials Generation","created":"2025-07-04","cited_by_count":9,"area":"diffusion"},{"id":"2405.03962","title":"AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion","created":"2024-05-07","cited_by_count":8,"area":null},{"id":"2602.00424","title":"Open Materials Generation with Inference-Time Reinforcement Learning","created":"2026-01-31","cited_by_count":8,"area":"deeprl"},{"id":"2406.09263","title":"Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers","created":"2024-06-13","cited_by_count":7,"area":"diffusion"},{"id":"2502.05970","title":"Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules","created":"2025-02-09","cited_by_count":7,"area":null},{"id":"2505.16790","title":"Learning Flexible Forward Trajectories for Masked Molecular Diffusion","created":"2025-05-22","cited_by_count":7,"area":"diffusion"},{"id":"2509.23822","title":"Space Group Conditional Flow Matching","created":"2025-09-28","cited_by_count":7,"area":"diffusion"},{"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":"2410.06502","title":"Chemistry-Inspired Diffusion with Non-Differentiable Guidance","created":"2024-10-09","cited_by_count":6,"area":"diffusion"},{"id":"2512.16607","title":"Boltzmann generators for amorphous particle systems","created":"2025-12-18","cited_by_count":6,"area":null},{"id":"2509.12212","title":"PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis","created":"2025-08-29","cited_by_count":5,"area":null},{"id":"2512.01756","title":"Mofasa: A Step Change in Metal-Organic Framework Generation","created":"2025-12-01","cited_by_count":5,"area":null},{"id":"2602.22251","title":"Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials","created":"2026-02-24","cited_by_count":5,"area":null},{"id":"2511.14426","title":"MiAD: Mirage Atom Diffusion for De Novo Crystal Generation","created":"2025-11-18","cited_by_count":4,"area":"diffusion"},{"id":"2512.06987","title":"OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction","created":"2025-12-07","cited_by_count":4,"area":null},{"id":"2605.14344","title":"CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation","created":"2026-05-14","cited_by_count":3,"area":"lr"},{"id":"2510.23181","title":"Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space","created":"2025-10-27","cited_by_count":2,"area":"diffusion"},{"id":"2605.08262","title":"SLayerGen: a Crystal Generative Model for all Space and Layer Groups","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2408.15601","title":"Grand canonical generative diffusion model for crystalline phases and grain boundaries","created":"2024-08-28","cited_by_count":1,"area":"diffusion"},{"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":"2606.00776","title":"Latent Diffusion Pretraining for Crystal Property Prediction","created":"2026-05-30","cited_by_count":1,"area":"diffusion"},{"id":"2607.12380","title":"SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems","created":"2026-07-14","cited_by_count":1,"area":"diffusion"},{"id":"2407.04557","title":"Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates","created":"2024-07-05","cited_by_count":0,"area":"diffusion"},{"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":"2512.07486","title":"Materium: An Autoregressive Approach for Material Generation","created":"2025-12-08","cited_by_count":0,"area":null},{"id":"2605.17254","title":"CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials","created":"2026-05-17","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":"2610.03880","title":"Reinforcement Learning on the Discrete Composition Channel of a Crystal Generator: Validated Gains and Reward Hacking","created":"2026-10-02","cited_by_count":0,"area":"diffusion"},{"id":"2610.14999","title":"DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Search","created":"2026-10-10","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}