用化学价态约束提升生成材料的合理性，避免无效结构。
Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
- 在生成流程中嵌入化学价态规则，确保原子组合合法。
- 生成物85%热力学稳定，68%声子稳定，性能优异。
- 可定制生成高性能半导体和介电材料，适合科研人员使用。
基于扩散的深度生成模型已成为逆向材料设计的强大工具。然而，现有方法常忽略氧化态平衡等关键化学约束，导致生成化学无效结构。本文提出CrysVCD（带价态约束设计的晶体生成器），一种将化学规则直接融入生成过程的模块化框架。CrysVCD首先使用基于Transformer的元素语言模型生成价态平衡的化学组成，再通过扩散模型生成晶体结构。该价态约束使化学价态检查效率相比纯数据驱动方法提升数个数量级。在稳定性指标微调后，CrysVCD实现85%热力学稳定性和68%声子稳定性。此外，支持功能材料的条件生成，成功发现高热导率半导体和高κ介电化合物等候选材料。作为通用插件，CrysVCD可集成至多种生成流程，提升化学合理性，为材料发现提供可靠、科学的基础路径。
原文摘要 · Abstract (English)
Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet, many existing approaches overlook essential chemical constraints such as oxidation state balance, which can lead to chemically invalid structures. Here we introduce CrysVCD (Crystal generator with Valence-Constrained Design), a modular framework that integrates chemical rules directly into the generative process. CrysVCD first employs a transformer-based elemental language model to generate valence-balanced compositions, followed by a diffusion model to generate crystal structures. The valence constraint enables orders-of-magnitude more efficient chemical valence checking, compared to pure data-driven approaches with post-screening. When fine-tuned on stability metrics, CrysVCD achieves 85% thermodynamic stability and 68% phonon stability. Moreover, CrysVCD supports conditional generation of functional materials, enabling discovery of candidates such as high thermal conductivity semiconductors and high-$κ$ dielectric compounds. Designed as a general-purpose plugin, CrysVCD can be integrated into diverse generative pipeline to promote chemical validity, offering a reliable, scientifically grounded path for materials discovery.
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