arXiv AI
CrystalAR: Autoregressive Modeling for Symmetric Crystal Generation
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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
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.
arXiv Machine Learning
Aug 14
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.
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 Machine Learning
Oct 2
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
The paper introduces CG-OMatG, an equivariant Riemannian flow-based generative model that predicts molecular crystal structures using a coarse-grained, hierarchical representation. It treats molecules as rigid bodies, performs inter- and intra-molecular message passing, and learns to reconstruct molecule centroids, orientations, and lattice parameters conditioned on chemical species and conformer geometry. The model is trained on OMC25 and CSD datasets, fine-tuned with policy gradient reinforcement learning to favor low-energy structures, and validated against a CSP blind test benchmark using COMPACK packing-similarity analysis.
arXiv Machine Learning
3d ago
SymADiT: Symmetry-Aware Crystal Generation with Latent Flow Matching
SymADiT is a symmetry‑aware variant of the All‑atom Diffusion Transformer (ADiT) designed for crystal generation. It uses Wyckoff‑based crystal representations and enforces symmetry constraints from the crystal’s space group and atom positions during latent‑space generative modeling. The approach yields stable, symmetric, and physically plausible crystals while maintaining competitive performance against both symmetry‑aware and symmetry‑agnostic models.