Crystal Structures
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Related Articles from SNS
Beyond Pairwise Interactions: Equivariant Hypergraph Diffusion for Crystal Structure Prediction
Announce Type: replace Abstract: Crystal Structure Prediction (CSP) remains a fundamental challenge with significant implications for materials discovery and the advancement of various scientific disciplines. Recent advances have demonstrated that generative models, particularly diffusion models, are especially promising for CSP.
Learning Thermoelectric Transport from Crystal Structures via Multiscale Graph Neural Network
arXiv:2512.06697v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are designed to extract latent patterns from graph-structured data, making them particularly well suited for crystal representation learning. Here, we propose a GNN model tailored for estimating electronic transport coefficients in inorganic thermoelectric crystals. The model encodes crystal structures and physicochemical properties in a multiscale manner, encompassing global, atomic, bond, and angular levels.
Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching
arXiv:2606.03199v1 Announce Type: cross Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per molecule. Generative models such as OXtal dramatically reduce this cost by sampling stable organic crystal structures directly. However, OXtal forgoes explicit lattice parametrization in favour of modelling large crops of the bulk material with expensive triangle layers, which can incur a...
Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching
arXiv:2606.03199v1 Announce Type: new Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per molecule. Generative models such as OXtal dramatically reduce this cost by sampling stable organic crystal structures directly. However, OXtal forgoes explicit lattice parametrization in favour of modelling large crops of the bulk material with expensive triangle layers, which can incur a...
Bacteria uncover distinct strategy to import rare sugar polymers, crystal structures show
Bacteria uncover distinct strategy to import rare sugar polymers, crystal structures show Sadie Harley Scientific Editor Andrew Zinin Lead Editor Even though sugars are often framed as simple sources of energy, they also serve as structurally complex and functionally diverse molecules that mediate interactions between organisms. Among these, β-1,2-glucans, which are a class of glucose-based polymers, stand out for their varied and sometimes subtle roles. Found across a wide range of...
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. Generative models, especially state-of-the-art diffusion models, offer the promise of modeling complex data distributions and proposing novel, realistic samples. However, current generative AI models still struggle to produce diverse, original, and reliable structures of experimentally achievable materials suitable...
An Efficient Parity-Blocked Method for Band-Structure Computation of 3D Anisotropic Phononic Crystals
new Abstract: Band-structure calculations for three-dimensional anisotropic phononic crystals require the repeated solution of large elastic generalized eigenvalue problems along Bloch paths. In standard staggered-grid discretizations, anisotropic coupling may involve derivative components located at incompatible grid positions, so additional interpolation or averaging closures are often introduced. This paper proposes a parity-blocked rotated staggered discretization based on four...
ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction
Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal structures exhibit infinite periodic arrangements of atoms, requiring methods capable of capturing both local and global information effectively. However, current works fall short of capturing long-range interactions within periodic structures.
Do AI Structure Predictors Capture Bound-State Disorder? A Benchmark on Fuzzy Protein Complexes
Fuzzy protein complexes, in which an intrinsically disordered protein (IDP) retains conformational disorder upon binding, pose a fundamental challenge for structure predictors trained on ordered systems, where crystal structures capture only the most ordered ensemble snapshot, making standard benchmarking metrics misleading. Here, we present the first systematic evaluation of AlphaFold3 (AF3), AlphaFold2-Multimer (AF2MM), Chai-1, and Boltz-2 on a curated dataset of fuzzy complexes from...
Light pulses uncover Higgs mode that reshapes perovskite crystal symmetry
Light pulses uncover Higgs mode that reshapes perovskite crystal symmetry Sadie Harley Scientific Editor Robert Egan Associate Editor Waves of light and sound interact to drive electronic and structural changes in a perovskite crystal. At the atomic scale, nothing is ever truly still. Materials that appear perfectly rigid and motionless to the naked eye are in fact swarms of vibrating atoms.