SLayerGen generates crystals invariant to any space or layer group via autoregressive lattice and Wyckoff sampling plus equivariant diffusion, achieving gains over bulk models on diperiodic materials after correcting a prior loss inconsistency for hexagonal groups.
Wyckoffdiff–a generative diffusion model for crystal symmetry.arXiv preprint arXiv:2502.06485
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
other 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
other 1polarities
unclear 1representative citing papers
A GPT-style model pretrained on 133M catalyst structures generates valid structures conditioned on categorical and continuous properties, achieving 98% structural validity and up to 4-fold screening efficiency gains.
CrystalReasoner combines LLM reasoning traces with physical priors and multi-objective RL to generate valid, stable, and property-conditioned crystal structures.
citing papers explorer
-
SLayerGen: a Crystal Generative Model for all Space and Layer Groups
SLayerGen generates crystals invariant to any space or layer group via autoregressive lattice and Wyckoff sampling plus equivariant diffusion, achieving gains over bulk models on diperiodic materials after correcting a prior loss inconsistency for hexagonal groups.
-
Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining
A GPT-style model pretrained on 133M catalyst structures generates valid structures conditioned on categorical and continuous properties, achieving 98% structural validity and up to 4-fold screening efficiency gains.
-
CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation
CrystalReasoner combines LLM reasoning traces with physical priors and multi-objective RL to generate valid, stable, and property-conditioned crystal structures.