mlsanityData to 7 Oct 2026

AI-driven inverse design of materials: Past, present and future

arXiv 2411.09429 ↗First posted 14 Nov 2024cond-mat.mtrl-sci cond-mat.supr-con cs.AIcited by 10

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Abstract (arXiv)

The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining issues for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 93 of its 418 references that are in the map. First posted on arXiv 14 Nov 2024.

Builds on

93 of 418 references.

References · 93

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  7. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  8. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  9. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  10. 2017Graph Attention Networkscited by 8,854
  11. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  12. 2017Inductive Representation Learning on Large Graphscited by 6,284
38 more
  1. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  4. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  5. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  6. 2021E(n) Equivariant Graph Neural Networkscited by 951
  7. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  8. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  9. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  10. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  11. 2020Directional Message Passing for Molecular Graphscited by 556
  12. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  13. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  14. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  15. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  16. 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 308
  17. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
  18. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 268
  19. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  20. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  21. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  22. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  23. 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
  24. 2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 225
  25. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
  26. 2022Graph neural networks for materials science and chemistrycited by 181
  27. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  28. 2022e3nn: Euclidean Neural Networkscited by 171
  29. 2021Spherical Message Passing for 3D Graph Networkscited by 166
  30. 2021Frame Averaging for Invariant and Equivariant Network Designcited by 166
  31. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
  32. 2022Equivariant 3D-Conditional Diffusion Models for Molecular Linker Designcited by 121
  33. 2023Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNscited by 113
  34. 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 105
  35. 2020LieTransformer: Equivariant self-attention for Lie Groupscited by 100
  36. 2023MatterGen: a generative model for inorganic materials designcited by 98
  37. 2023On the Expressive Power of Geometric Graph Neural Networkscited by 89
  38. 2022Periodic Graph Transformers for Crystal Material Property Predictioncited by 87

50 most cited of 93 All 93 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 2.

10 papers.

  1. 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
  2. 2025InvDesFlow-AL: active learning-based workflow for inverse design of functional materialscited by 4
  3. 2025Generative Models for Crystalline Materialscited by 3
  4. 2025PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generationcited by 3
  5. 2025Enhancing Materials Discovery with Valence Constrained Design in Generative Modelingcited by 2
  6. 2025Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergycited by 1
  7. 2025Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental designcited by 1
  8. 2025High-temperature superconductivity in Li2AuH6 mediated by strong electron-phonon coupling under ambient pressurecited by 0
  9. 2026Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learningcited by 0
  10. 2026LEAP: A closed-loop framework for perovskite precursor additive discoverycited by 0