arXiv Machine Learning
Aug 10
DynaCrys: Crystal Generation with Dynamic Space-Group Diffusion
arXiv:2608. 07401v1 Announce Type: cross Abstract: The search for new crystalline materials spans an enormous compositional and structural space.
Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
arXiv:2608. 13457v1 Announce Type: new Abstract: Generating crystals has recently attracted significant interest due to their broad applications in materials science.
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arXiv AI
Jul 7
NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction
arXiv:2602. 17176v4 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding.
arXiv Machine Learning
Sep 23
Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence
The study benchmarks 12 deep generative crystal structure prediction models against the template-based TCSP 2.0 on 180 test structures, finding that template retrieval achieves the highest top‑1 success (68.3%). Most generative predictions overlap with template substitutions, and removing entire stoichiometric prototype families from training reduces accuracy by 50‑78%, indicating strong prototype dependence. Only a small subset of predictions remain after such removal, suggesting limited genuine de‑novo capability.
arXiv AI
4d ago
SymDrift: One-Shot Generative Modeling under Symmetries
arXiv:2605.06140v3 Announce Type: replace-cross Abstract: Generative modeling of physical systems, such as molecules, requires learning distributions that are invariant under global symmetries, such...
arXiv Machine Learning
2d ago
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
arXiv:2609.39773v1 Announce Type: new Abstract: Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models...
arXiv AI
Jun 3
Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation
arXiv:2604. 13354v2 Announce Type: replace-cross Abstract: The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science.
arXiv Machine Learning
Jun 15
XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models
arXiv:2606. 14003v1 Announce Type: cross Abstract: Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science.
arXiv AI
4d ago
Where Should Physics Enter a Molecular Crystal Generator?
The paper introduces CrystAF, an all‑atom crystal flow‑map generation model, and evaluates where physics should be incorporated into generative crystal structure models. By applying physics‑informed post‑training, the authors improve molecular validity and crystal packing without altering sampling speed, while inference‑time corrections further refine the structures. The study demonstrates that post‑training and inference‑time physics are complementary, and that the post‑training approach transfers to other generators such as Clari‑M and MolCrystalFlow.
arXiv Machine Learning
4d ago
GLASS: Global Latent Aggregation with Slot-based Set Decoding for Scalable All-Atom Crystal Generation
arXiv:2609.37158v1 Announce Type: new Abstract: Generative models for crystals enable the discovery of novel structures, but scaling all-atom generation to larger systems such as metal--organic frame...
arXiv AI
Jul 7
Interpretable Nanoporous Materials Design with Symmetry-Aware Networks
arXiv:2509. 15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design.
arXiv Machine Learning
Jul 1
Diffusion-warm sampling of the XY model enables fast thermalization at scale
arXiv:2606. 30773v1 Announce Type: cross Abstract: We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models.
arXiv AI
Jul 2
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
arXiv:2604. 02270v2 Announce Type: replace-cross Abstract: Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample.