mlsanityData to 7 Oct 2026

Rigid Body Flows for Sampling Molecular Crystal Structures

arXiv 2301.11355 ↗First posted 26 Jan 2023cs.LG physics.chem-ph physics.comp-phcited by 27

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Abstract (arXiv)

Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibility and expressiveness. In this work, we introduce a new type of normalizing flow that is tailored for modeling positions and orientations of multiple objects in three-dimensional space, such as molecules in a crystal. Our approach is based on two key ideas: first, we define smooth and expressive flows on the group of unit quaternions, which allows us to capture the continuous rotational motion of rigid bodies; second, we use the double cover property of unit quaternions to define a proper density on the rotation group. This ensures that our model can be trained using standard likelihood-based methods or variational inference with respect to a thermodynamic target density. We evaluate the method by training Boltzmann generators for two molecular examples, namely the multi-modal density of a tetrahedral system in an external field and the ice XI phase in the TIP4P water model. Our flows can be combined with flows operating on the internal degrees of freedom of molecules and constitute an important step towards the modeling of distributions of many interacting molecules.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 41 of its 83 references that are in the map. First posted on arXiv 26 Jan 2023.

Builds on

41 of 83 references.

References · 41

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29 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 7; 2025: 12; 2026: 1.

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  2. 2024Iterated Denoising Energy Matching for Sampling from Boltzmann Densitiescited by 113
  3. 2025Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matchingcited by 78
  4. 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicscited by 62
  5. 2023Clifford Group Equivariant Neural Networkscited by 54
  6. 2024Transferable Boltzmann Generatorscited by 43
  7. 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 40
  8. 2023SE(3) Equivariant Augmented Coupling Flowscited by 37
  9. 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generatorscited by 36
  10. 2024Efficient mapping of phase diagrams with conditional Boltzmann Generatorscited by 18
  11. 2025FALCON: Few-step Accurate Likelihoods for Continuous Flowscited by 12
  12. 2025Amortized Sampling with Transferable Normalizing Flowscited by 11
15 more
  1. 2025Scalable Boltzmann Generators for equilibrium sampling of large-scale materialscited by 11
  2. 2025ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generationcited by 10
  3. 2025UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomoleculescited by 7
  4. 2025Space Group Conditional Flow Matchingcited by 7
  5. 2024Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptidescited by 6
  6. 2023Generative Marginalization Modelscited by 4
  7. 2023Topological Obstructions and How to Avoid Themcited by 4
  8. 2024Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Posecited by 3
  9. 2025Generative Models for Crystalline Materialscited by 3
  10. 2025Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coveragecited by 2
  11. 2026Charting Empirical Laws for LLM Fine-Tuning in Scientific Multi-Discipline Learningcited by 2
  12. 2023Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbationcited by 0
  13. 2024Flow Perturbation to Accelerate Unbiased Sampling of Boltzmann distributioncited by 0
  14. 2025Operator Forces For Coarse-Grained Molecular Dynamicscited by 0
  15. 2025FRAGMENTA: End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimizationcited by 0