Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction

• URL: http://arxiv.org/abs/2502.09423v1
• Date: Thu, 13 Feb 2025 15:45:36 GMT
• Title: Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction
• Authors: Ziyi Chen, Yang Yuan, Siming Zheng, Jialong Guo, Sihan Liang, Yangang Wang, Zongguo Wang,
• Abstract summary: We propose a Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction (TransVAE-CSP) TransVAE-CSP learns the characteristic distribution space of stable materials, enabling both the reconstruction and generation of crystal structures. 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.
• Score: 16.846289696048647
• License:
• Abstract: 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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