用Transformer增强的生成模型，精准捕捉晶体周期性与对称性，提升结构预测与生成效果。
Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction
- 基于等变点积注意力的Transformer编码器，融合距离扩展与不可约表示。
- 在碳、钙钛矿和材料项目数据集上，重建与生成性能均超越现有方法。
- 适合材料设计与逆向合成研究者使用，尤其关注晶体结构生成任务。
晶体结构是理解材料物理化学性质的基础。生成模型已成为晶体结构预测（CSP）的新范式，但准确捕捉晶体结构的关键特征——如周期性和对称性——仍具挑战。本文提出一种基于Transformer增强的变分自编码器（TransVAE-CSP），学习稳定材料的特征分布空间，实现晶体结构的重建与生成。该模型结合自适应距离扩展与不可约表示，有效捕获晶体的周期性与对称性；编码器采用基于等变点积注意力机制的Transformer网络。在carbon_24、perov_5和mp_20数据集上的实验表明，TransVAE-CSP在多种建模指标下均优于现有方法，为晶体结构设计与优化提供了有力工具。
原文摘要 · Abstract (English)
Crystal structure forms the foundation for understanding the physical and chemical properties of materials. Generative models have emerged as a new paradigm in crystal structure prediction(CSP), however, accurately capturing key characteristics of crystal structures, such as periodicity and symmetry, remains a significant challenge. In this paper, we propose a Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction (TransVAE-CSP), who learns the characteristic distribution space of stable materials, enabling both the reconstruction and generation of crystal structures. TransVAE-CSP integrates adaptive distance expansion with irreducible representation to effectively capture the periodicity and symmetry of crystal structures, and the encoder is a transformer network based on an equivariant dot product attention mechanism. Experimental results on the carbon_24, perov_5, and mp_20 datasets demonstrate that TransVAE-CSP outperforms existing methods in structure reconstruction and generation tasks under various modeling metrics, offering a powerful tool for crystal structure design and optimization.
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