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 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
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.
arXiv Machine Learning
Jul 24
SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
arXiv:2505. 19619v3 Announce Type: replace Abstract: Deep generative models have recently garnered significant attention across various fields, from physics to chemistry, where sampling from unnormalized Boltzmann-like distributions represents a fundamental challenge.
arXiv Machine Learning
Aug 6
Group-Equivariant Diffusion Models for Lattice Field Theory
arXiv:2510. 26081v2 Announce Type: replace-cross Abstract: Near the critical point, Markov Chain Monte Carlo (MCMC) simulations of lattice quantum field theories (LQFT) become increasingly inefficient due to critical slowing down.
arXiv Machine Learning
Jul 24
Boltzmann generators for amorphous particle systems
arXiv:2512. 16607v2 Announce Type: replace-cross Abstract: Sampling configurations in thermodynamic equilibrium is a long-standing challenge in statistical physics.
arXiv AI
Jul 29
DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction
arXiv:2607. 24785v1 Announce Type: cross Abstract: Efficient exploration of the photonic crystal (PhC) lattice design space is essential for developing photonic crystal surface-emitting lasers.
arXiv AI
Jun 2
Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
arXiv:2603. 23398v3 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design.