mlsanityData to 13 Oct 2026

MatterGen: a generative model for inorganic materials design

arXiv 2312.03687 ↗First posted 6 Dec 2023cond-mat.mtrl-sci cs.AIcited by 98

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Cited by 22 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generating entirely novel materials given desired property constraints. Despite recent progress, current generative models have low success rate in proposing stable crystals, or can only satisfy a very limited set of property constraints. Here, we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, we introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice. We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset. Compared to prior generative models, structures produced by MatterGen are more than twice as likely to be novel and stable, and more than 15 times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, novel materials with desired chemistry, symmetry, as well as mechanical, electronic and magnetic properties. Finally, we demonstrate multi-property materials design capabilities by proposing structures that have both high magnetic density and a chemical composition with low supply-chain risk. We believe that the quality of generated materials and the breadth of MatterGen's capabilities represent a major advancement towards creating a universal generative model for materials design.

Key facts. Cited by 98 papers in the map (data through 13 Oct 2026). Builds on 21 of its 104 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

21 of 104 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,106
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,093
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,028
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,023
  6. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,564
  7. 2022Classifier-Free Diffusion Guidancecited by 5,785
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,077
  9. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,685
  10. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,039
  11. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 672
  12. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
9 more
  1. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
  2. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  3. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 171
  4. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 145
  5. 2023Scalable Diffusion for Materials Generationcited by 53
  6. 2018CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networkscited by 31
  7. 2023Crystal-GFN: sampling crystals with desirable properties and constraintscited by 31
  8. 2023Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learningcited by 26
  9. 2022Large-scale machine-learning-assisted exploration of the whole materials spacecited by 13

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 41; 2025: 38; 2026: 18.

98 papers.

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  1. 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 108
  2. 2024Fine-Tuned Language Models Generate Stable Inorganic Materials as Textcited by 93
  3. 2025All-atom Diffusion Transformers: Unified generative modelling of molecules and materialscited by 75
  4. 2024Space Group Constrained Crystal Generationcited by 72
  5. 2024Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generationcited by 54
  6. 2024FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributionscited by 52
  7. 2024Does equivariance matter at scale?cited by 51
  8. 2025A Fourier Space Perspective on Diffusion Modelscited by 50
  9. 2025SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Modelscited by 49
  10. 2024Training of Physical Neural Networkscited by 48
  11. 2024LLMatDesign: Autonomous Materials Discovery with Large Language Modelscited by 42
  12. 2024Are LLMs Ready for Real-World Materials Discovery?cited by 40
38 more
  1. 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 40
  2. 2024Time Series Diffusion in the Frequency Domaincited by 36
  3. 2024LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillationcited by 32
  4. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  5. 2024Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operatorscited by 26
  6. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 26
  7. 2024Materials science in the era of large language models: a perspectivecited by 24
  8. 2025FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generationcited by 20
  9. 2025Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functionalcited by 19
  10. 2024MatExpert: Decomposing Materials Discovery by Mimicking Human Expertscited by 18
  11. 2025MatterChat: A Multi-Modal LLM for Material Sciencecited by 18
  12. 2025A Periodic Bayesian Flow for Material Generationcited by 16
  13. 2025Periodic Materials Generation using Text-Guided Joint Diffusion Modelcited by 15
  14. 2025Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuningcited by 15
  15. 2024Generative Hierarchical Materials Searchcited by 13
  16. 2024Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Datacited by 12
  17. 2025Invariant Tokenization of Crystalline Materials for Language Model Enabled Generationcited by 12
  18. 2025Provable Maximum Entropy Manifold Exploration via Diffusion Modelscited by 12
  19. 2025LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generationcited by 12
  20. 2024Exploring Discrete Flow Matching for 3D De Novo Molecule Generationcited by 11
  21. 2024End-to-End Crystal Structure Prediction from Powder X-Ray Diffractioncited by 10
  22. 2024Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamicscited by 10
  23. 2024AI-driven inverse design of materials: Past, present and futurecited by 10
  24. 2024NeuralDEM -- Real-time Simulation of Industrial Particulate Flowscited by 10
  25. 2025Establishing baselines for generative discovery of inorganic crystalscited by 9
  26. 2025Symmetry-Aware Bayesian Flow Networks for Crystal Generationcited by 9
  27. 2024AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusioncited by 8
  28. 2025Siamese Foundation Models for Crystal Structure Predictioncited by 8
  29. 2025Artificial Intelligence and Generative Models for Materials Discovery -- A Reviewcited by 8
  30. 2024Improving Antibody Design with Force-Guided Sampling in Diffusion Modelscited by 7
  31. 2024Generative Inverse Design of Crystal Structures via Diffusion Models with Transformerscited by 7
  32. 2025Known Unknowns: Out-of-Distribution Property Prediction in Materials and Moleculescited by 7
  33. 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
  34. 2025Space Group Conditional Flow Matchingcited by 7
  35. 2026MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamicscited by 7
  36. 2026Autonomous Research Agents: A Survey of AI Scientists and the Verification Gapcited by 7
  37. 2023Generative AI Beyond LLMs: System Implications of Multi-Modal Generationcited by 6
  38. 2024Zero Shot Molecular Generation via Similarity Kernelscited by 6

50 most cited of 98 All 98 (JSON)

Similar, not cited

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  1. 2026OxiGen: Oxidation-State-Aware Crystal Generation
  2. 2026Chemical filters for ultra-high-throughput materials screening and generation
  3. 2025Open Materials Generation with Stochastic Interpolants
  4. 2025WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
  5. 2025CrystalGRW: Generative Modeling of Crystal Structures with Targeted Properties via Geodesic Random Walks
  6. 2023Towards Symmetry-Aware Generation of Periodic Materials
  7. 2025CrystalDiT: A Diffusion Transformer for Crystal Generation
  8. 2025Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling