Rigid Body Flows for Sampling Molecular Crystal Structures
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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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- 2021Efficient and Modular Implicit Differentiationcited by 199
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- 2020Stochastic Normalizing Flowscited by 156
- 2018Neural Importance Samplingcited by 154
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Citing papers per year: 2023: 7; 2024: 7; 2025: 12; 2026: 1.
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- 2024Transferable Boltzmann Generatorscited by 43
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- 2023SE(3) Equivariant Augmented Coupling Flowscited by 37
- 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generatorscited by 36
- 2024Efficient mapping of phase diagrams with conditional Boltzmann Generatorscited by 18
- 2025FALCON: Few-step Accurate Likelihoods for Continuous Flowscited by 12
- 2025Amortized Sampling with Transferable Normalizing Flowscited by 11
- 2025Scalable Boltzmann Generators for equilibrium sampling of large-scale materialscited by 11
- 2025ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generationcited by 10
12 most cited of 21 All 21 (JSON)
- 2025FALCON: Few-step Accurate Likelihoods for Continuous Flows10 Dec 2025
- 2025Generative Models for Crystalline Materials27 Nov 2025
- 2025Scalable Boltzmann Generators for equilibrium sampling of large-scale materials29 Sep 2025
- 2025Amortized Sampling with Transferable Normalizing Flows25 Aug 2025
- 2025Operator Forces For Coarse-Grained Molecular Dynamics24 Jun 2025
- 2025UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules20 May 2025
- 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generators25 Feb 2025
- 2025ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation20 Feb 2025
- 2024Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides27 Aug 2024
- 2024Flow Perturbation to Accelerate Unbiased Sampling of Boltzmann distribution15 Jul 2024
- 2024Transferable Boltzmann Generators20 Jun 2024
- 2024Efficient mapping of phase diagrams with conditional Boltzmann Generators18 Jun 2024
12 newest of 21 All 21 (JSON)