mlsanityData to 14 Oct 2026

Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey

arXiv 2505.16379 ↗First posted 22 May 2025cond-mat.mtrl-sci cs.AIcited by 10

Not on the map. Why?

Abstract (arXiv)

Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design new materials with tailored properties is critical to solving some of the most pressing global challenges. In recent years, the growing availability of high-quality materials data combined with rapid advances in Artificial Intelligence (AI) has opened new opportunities for accelerating materials discovery. Data-driven generative models provide a powerful tool for materials design by directly create novel materials that satisfy predefined property requirements. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. To fill this gap, this paper provides a comprehensive overview of recent progress in AI-driven materials generation. We first organize various types of materials and illustrate multiple representations of crystalline materials. We then provide a detailed summary and taxonomy of current AI-driven materials generation approaches. Furthermore, we discuss the common evaluation metrics and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future directions and challenges in this fast-growing field. The related sources can be found at https://github.com/ZhixunLEE/Awesome-AI-for-Materials-Generation.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 69 of its 145 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

69 of 145 references.

References · 69

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  6. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  8. 2020Scaling Laws for Neural Language Modelscited by 6,586
  9. 2022Classifier-Free Diffusion Guidancecited by 5,847
  10. 2022Generative Adversarial Networkscited by 5,627
  11. 2024Qwen2.5 Technical Reportcited by 4,942
  12. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
38 more
  1. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2017Improved Training of Wasserstein GANscited by 2,235
  4. 2021E(n) Equivariant Graph Neural Networkscited by 965
  5. 2022Autoregressive Image Generation using Residual Quantizationcited by 722
  6. 2017Deep Learning Scaling is Predictable, Empiricallycited by 582
  7. 2020Directional Message Passing for Molecular Graphscited by 561
  8. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
  9. 2015An Introduction to Convolutional Neural Networkscited by 514
  10. 2015Variational Dropout and the Local Reparameterization Trickcited by 490
  11. 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 354
  12. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 343
  13. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
  14. 2023Flow Matching on General Geometriescited by 265
  15. 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
  16. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  17. 2020Generalization and Representational Limits of Graph Neural Networkscited by 218
  18. 2020Machine Learning Force Fieldscited by 207
  19. 2019Universal Invariant and Equivariant Graph Neural Networkscited by 183
  20. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 180
  21. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 172
  22. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 148
  23. 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 109
  24. 2024Fine-Tuned Language Models Generate Stable Inorganic Materials as Textcited by 93
  25. 2018IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesiscited by 85
  26. 2025All-atom Diffusion Transformers: Unified generative modelling of molecules and materialscited by 75
  27. 2024Space Group Constrained Crystal Generationcited by 73
  28. 2023Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB filescited by 59
  29. 2023Scalable Diffusion for Materials Generationcited by 54
  30. 2020An invertible crystallographic representation for general inverse design of inorganic crystals with targeted propertiescited by 53
  31. 2024FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributionscited by 53
  32. 2024Open Materials 2024 (OMat24) Inorganic Materials Dataset and Modelscited by 52
  33. 2025SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Modelscited by 51
  34. 2019Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materialscited by 46
  35. 2022Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraintscited by 30
  36. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  37. 2023SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biasescited by 27
  38. 2023Towards Symmetry-Aware Generation of Periodic Materialscited by 26

50 most cited of 69 All 69 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2025Continuous SUN (Stable, Unique, and Novel) Metric for Generative Modeling of Inorganic Crystalscited by 6
  2. 2025DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Modelscited by 5
  3. 2025Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffractioncited by 4
  4. 2025Generative Models for Crystalline Materialscited by 4
  5. 2026Substitution-Based Analysis of Structural Novelty for Generative Models of Materialscited by 3
  6. 2025Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental designcited by 1
  7. 2025Preference Learning from Physics-Based Feedback: Tuning Language Models to Design BCC/B2 Superalloyscited by 1
  8. 2025Open-Set Living Need Prediction with Large Language Modelscited by 0
  9. 2026Generative Inverse Design of Cold Metals for Low-Power Electronicscited by 0
  10. 2026Perspective: Towards sustainable exploration of chemical spaces with machine learningcited by 0