VQCrystal：利用向量量化发现稳定晶体结构
VQCrystal: Leveraging Vector Quantization for Discovery of Stable Crystal Structures
AI总结：
提出 VQCrystal 框架，融合分层 VQ-VAE、IAP 模型与遗传算法进行晶体逆向设计，并在三维和二维材料生成验证中展现高有效性、多样性与稳定性。
摘要：
通过计算方法发现功能性晶体材料仍是材料科学中的一项艰巨挑战。本文提出 VQCrystal，这是一种创新的深度学习框架，利用离散潜在表征来克服当前晶体生成与逆向设计方法中的关键局限。VQCrystal 采用分层 VQ-VAE 架构编码全局和原子级晶体特征，并结合基于机器学习的原子间势（IAP）模型与遗传算法，实现面向性能目标的逆向设计。在多个不同数据集上的基准评估表明，VQCrystal 在表征学习和新型晶体发现方面具有先进能力。值得注意的是，VQCrystal 在 MP-20 上取得了最先进的性能，力有效性达到 91.93%，Fréchet 距离为 0.152，表明采样过程兼具强有效性和高多样性。为证明实际适用性，我们将 VQCrystal 应用于三维和二维材料设计。对于三维材料，密度泛函理论验证确认，在 56 种筛选出的材料中，63.04% 的带隙和 99% 的形成能符合目标范围。此外，437 种生成材料被验证为训练集之外完整数据库中的已有条目。对于二维材料发现，在 23 种筛选结构中，73.91% 表现出高稳定性，形成能低于 -1 eV/atom。我们的结果突显了 VQCrystal 加速发现具有定制性能的新型材料的潜力。
英文摘要：
Discovering functional crystalline materials through computational methods remains a formidable challenge in materials science. Here, we introduce VQCrystal, an innovative deep learning framework that leverages discrete latent representations to overcome key limitations in current approaches to crystal generation and inverse design. VQCrystal employs a hierarchical VQ-VAE architecture to encode global and atom-level crystal features, coupled with a machine learning-based inter-atomic potential(IAP) model and a genetic algorithm to realize property-targeted inverse design. Benchmark evaluations on diverse datasets demonstrate VQCrystal's advanced capabilities in representation learning and novel crystal discovery. Notably, VQCrystal achieves state-of-the-art performance with 91.93\% force validity and a Fréchet Distance of 0.152 on MP-20, indicating both strong validity and high diversity in the sampling process. To demonstrate real-world applicability, we apply VQCrystal for both 3D and 2D material design. For 3D materials, the density-functional theory validation confirmed that 63.04\% of bandgaps and 99\% of formation energies of the 56 filtered materials matched the target range. Moreover, 437 generated materials were validated as existing entries in the full database outside the training set. For the discovery of 2D materials, 73.91\% of 23 filtered structures exhibited high stability with formation energies below -1 eV/atom. Our results highlight VQCrystal's potential to accelerate the discovery of novel materials with tailored properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
