Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce complete crystallographic specifications, limiting their ability to capture global symmetry and structural dependencies. In particular, current state-of-the-art approaches generate crystals only up to site symmetries and rely on sampling space groups from empirical distributions during generation. Inspired by \emph{spontaneous symmetry breaking} in physics, where crystals break symmetries under external conditions, we prop
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Symmetry-Breaking De Novo Crystal Generation via Markovian J” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Symmetry-Breaking De Novo Crystal Generation via Markovian J” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Symmetry-Breaking De Novo Crystal Generation via Markovian J” ≈ “stabilityai/stable-diffusion-3.5-large””
- PossiblePossibly related (embedding) · 53%Guiding generative models to uncover diverse and novel crystals via reinforcement learning →
- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- LinkedLinked via arxiv author · 85%Van Khoa Nguyen →
“Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion”
- LinkedLinked via arxiv author · 85%Alexandros Kalousis →
“Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion”