mlsanityData to 7 Oct 2026

Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation

arXiv 2503.00152 ↗First posted 28 Feb 2025cs.LG cond-mat.mtrl-scicited by 12

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Abstract (arXiv)

We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 12 of its 42 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

12 of 42 references.

References · 12

  1. 2018Hierarchical Neural Story Generationcited by 1,182
  2. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 268
  3. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
  4. 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 105
  5. 2023MatterGen: a generative model for inorganic materials designcited by 98
  6. 2024Fine-Tuned Language Models Generate Stable Inorganic Materials as Textcited by 92
  7. 2022Periodic Graph Transformers for Crystal Material Property Predictioncited by 87
  8. 2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
  9. 2023Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB filescited by 59
  10. 2023Efficient Approximations of Complete Interatomic Potentials for Crystal Property Predictioncited by 37
  11. 2024Complete and Efficient Graph Transformers for Crystal Material Property Predictioncited by 34
  12. 2023Towards Symmetry-Aware Generation of Periodic Materialscited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 11; 2026: 1.

12 papers.

  1. 2025Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodalitycited by 49
  2. 2025Space Group Equivariant Crystal Diffusioncited by 15
  3. 2025Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physicscited by 11
  4. 2025Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientistscited by 10
  5. 2025Materials Generation in the Era of Artificial Intelligence: A Comprehensive Surveycited by 9
  6. 2025Continuous SUN (Stable, Unique, and Novel) Metric for Generative Modeling of Inorganic Crystalscited by 5
  7. 2025InertialAR: Autoregressive 3D Molecule Generation with Inertial Framescited by 3
  8. 2025Generative Models for Crystalline Materialscited by 3
  9. 2026SLayerGen: a Crystal Generative Model for all Space and Layer Groupscited by 2
  10. 2025L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworkscited by 1
  11. 2025Preference Learning from Physics-Based Feedback: Tuning Language Models to Design BCC/B2 Superalloyscited by 1
  12. 2025Challenges in Non-Polymeric Crystal Structure Prediction: Why a Geometric, Permutation-Invariant Loss is Neededcited by 0