← 研踪 PaperTrailarXiv:2605.10115 · v2
SymADiT: Symmetry-Aware Crystal Generation with Latent Flow Matching
首次提交 2026-05-11 15:32
版本更新 2026-10-26 14:18
雷达观测 2026-10-29 00:36
摘要
Tackling the task of crystal generation, we aim to enhance the previously proposed, symmetry-agnostic, All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-aware variant. To do so, we use a representation of crystals based on Wyckoff positions. We follow ADiT and perform generative modelling in latent space, adapted to our symmetry-aware representation. By forcing the output of the generative model to adhere to the symmetry restrictions imposed by the generated crystal's space group and each atom's Wyckoff position, the generated crystals exhibit more realistic symmetry properties. We benchmark our method against both symmetry-aware and symmetry-agnostic models for crystal generation and show competitive performance, generating stable, symmetric, and physically plausible crystals with only an off-the-shelf Transformer architecture. Our code is available at https://github.com/Karmush21/SymADiT.
作者备注：Accepted at NeurIPS 2026. Code: https://github.com/Karmush21/SymADiT
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v2 · 2026-10-26 14:18 · 最新观测版本
SymADiT: Symmetry-Aware Crystal Generation with Latent Flow Matching
Tackling the task of crystal generation, we aim to enhance the previously proposed, symmetry-agnostic, All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-aware variant. To do so, we use a representation of crystals based on Wyckoff positions. We follow ADiT and perform generative modelling in latent space, adapted to our symmetry-aware representation. By forcing the output of the generative model to adhere to the symmetry restrictions imposed by the generated crystal's space group and each atom's Wyckoff position, the generated crystals exhibit more realistic symmetry properties. We benchmark our method against both symmetry-aware and symmetry-agnostic models for crystal generation and show competitive performance, generating stable, symmetric, and physically plausible crystals with only an off-the-shelf Transformer architecture. Our code is available at https://github.com/Karmush21/SymADiT.
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