# Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction

arXiv: https://arxiv.org/abs/2502.09423 · HTML: https://mlsanity.com/paper/2502.09423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [All that structure matches does not glitter](https://mlsanity.com/paper/2509.12178/) (2025)
- [Generative Inverse Design of Cold Metals for Low-Power Electronics](https://mlsanity.com/paper/2603.13920/) (2026)

## Abstract (arXiv)

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.