SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models

• URL: http://arxiv.org/abs/2502.03638v1
• Date: Wed, 05 Feb 2025 21:48:48 GMT
• Title: SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
• Authors: Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh,
• Abstract summary: We propose SymmCD, a novel generative model that incorporates crystallographic symmetry into the generative process. We decompose crystals into two components and learn their joint distribution through diffusion. We showcase the competitive performance of SymmCD on a subset of the Materials Project, obtaining diverse and valid crystals with realistic symmetries and predicted properties.
• Score: 20.39521603857931
• License:
• Abstract: Generating novel crystalline materials has potential to lead to advancements in fields such as electronics, energy storage, and catalysis. The defining characteristic of crystals is their symmetry, which plays a central role in determining their physical properties. However, existing crystal generation methods either fail to generate materials that display the symmetries of real-world crystals, or simply replicate the symmetry information from examples in a database. To address this limitation, we propose SymmCD, a novel diffusion-based generative model that explicitly incorporates crystallographic symmetry into the generative process. We decompose crystals into two components and learn their joint distribution through diffusion: 1) the asymmetric unit, the smallest subset of the crystal which can generate the whole crystal through symmetry transformations, and; 2) the symmetry transformations needed to be applied to each atom in the asymmetric unit. We also use a novel and interpretable representation for these transformations, enabling generalization across different crystallographic symmetry groups. We showcase the competitive performance of SymmCD on a subset of the Materials Project, obtaining diverse and valid crystals with realistic symmetries and predicted properties.

Related papers

• WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry [10.650073214237207]
  We propose a generative model, Wyckoff Diffusion, which generates symmetry-based descriptions of crystals. This is enabled by considering a crystal structure representation that encodes all symmetry. In addition to respecting symmetry by construction, the discrete nature of our model enables fast generation.
  arXiv  Detail & Related papers  (2025-02-10T14:04:23Z)
• CrystalGRW: Generative Modeling of Crystal Structures with Targeted Properties via Geodesic Random Walks [1.2141052067494946]
  We introduce CrystalGRW, a diffusion-based generative model that can predict stable phases validated by density functional theory. CrystalGRW demonstrates the ability to generate realistic crystal structures that are close to their ground states with accuracy comparable to existing models. These features help accelerate materials discovery and inverse design by offering stable, symmetry-consistent crystal candidates for experimental validation.
  arXiv  Detail & Related papers  (2025-01-15T18:26:35Z)
• A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction [89.38877696273364]
  We consider the prediction of general tensor properties of crystalline materials. We propose a General Materials Network (GMTNet), which is carefully designed to satisfy the required symmetries. Experimental results show that our GMTNet achieves promising performance on crystal tensors of various orders.
  arXiv  Detail & Related papers  (2024-06-03T16:26:16Z)
• Higher-order topology protected by latent crystalline symmetries [0.0]
  It is sufficient to have a latent rotation symmetry, which may be revealed upon performing an isospectral reduction on the system. This work extends the classification of topological crystalline insulators to include latent symmetries.
  arXiv  Detail & Related papers  (2024-05-04T16:21:24Z)
• Space Group Informed Transformer for Crystalline Materials Generation [2.405914457225118]
  We introduce CrystalFormer, a transformer-based autoregressive model specifically designed for space group-controlled generation of crystalline materials. The incorporation of space group symmetry significantly simplifies the crystal space, which is crucial for data and compute efficient generative modeling of crystalline materials.
  arXiv  Detail & Related papers  (2024-03-23T06:01:45Z)
• Complete and Efficient Graph Transformers for Crystal Material Property Prediction [53.32754046881189]
  Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. We introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom. We propose ComFormer, a SE(3) transformer designed specifically for crystalline materials.
  arXiv  Detail & Related papers  (2024-03-18T15:06:37Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Towards Symmetry-Aware Generation of Periodic Materials [64.21777911715267]
  We propose SyMat, a novel material generation approach that can capture physical symmetries of periodic material structures. SyMat generates atom types and lattices of materials through generating atom type sets, lattice lengths and lattice angles with a variational auto-encoder model. We show that SyMat is theoretically invariant to all symmetry transformations on materials and demonstrate that SyMat achieves promising performance on random generation and property optimization tasks.
  arXiv  Detail & Related papers  (2023-07-06T01:05:34Z)
• Equivariant Parameter Sharing for Porous Crystalline Materials [4.271235935891555]
  Existing methods for crystal property prediction either have constraints that are too restrictive or only incorporate symmetries between unit cells. We develop a model which incorporates the symmetries of the unit cell of a crystal in its architecture and explicitly models the porous structure. Our results confirm that our method performs better than existing methods for crystal property prediction and that the inclusion of symmetry results in a more efficient model.
  arXiv  Detail & Related papers  (2023-04-04T08:33:13Z)
• Multipartite spatial entanglement generated by concurrent nonlinear processes [91.3755431537592]
  Continuous variables multipartite entanglement is a key resource for quantum technologies. This work considers the multipartite entanglement generated in separated spatial modes of the same light beam by three different parametric sources.
  arXiv  Detail & Related papers  (2021-11-09T17:15:13Z)
• Inverse Learning of Symmetries [71.62109774068064]
  We learn the symmetry transformation with a model consisting of two latent subspaces. Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser. Our model outperforms state-of-the-art methods on artificial and molecular datasets.
  arXiv  Detail & Related papers  (2020-02-07T13:48:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.