In recent years, generative artificial intelligence has made significant advances in the design of crystalline materials, giving rise to approaches based on graph neural networks, diffusion models, and large language models. Existing evaluations commonly follow the stability-uniqueness-novelty (S.U.N.) framework, where stability is primarily assessed using thermodynamic criteria, which do not fully capture the dynamical stability essential for a material's practical existence. Dynamical stability is a key determinant of whether a material can be synthesized and persist, with phonon spectrum calculations serving as the standard for its evaluation. However, the high computational cost of such calculations has prevented large-scale assessment of dynamical stability in generated crystals. In this work, we introduce PhononBench, the first large-scale benchmark for dynamical stability in AI-generated crystals. Leveraging the recently developed MatterSim interatomic potential, which achieves density-functional-theory (DFT)-level accuracy in phonon predictions across more than 10,000 materials, PhononBench enables efficient phonon calculations and dynamical-stability analysis for 133,838 crystal structures generated by 7 leading crystal generation models. PhononBench reveals a widespread limitation of current generative models: unless otherwise specified, all reported dynamical-stability metrics are evaluated at a phonon-frequency threshold of -0.1 THz, with the average dynamical-stability rate across all generated structures being only 32.15%, and the top-performing model, MatterGen, reaching just 45.05%.In addition, we identify 32,995 crystal structures that are phonon-stable across the entire Brillouin zone under a strict threshold of -0.001 THz. In addition, a web-based service is accessible at http://phononbench.cn/, enabling minute-level ultra-fast phonon predictions.

翻译：近年来，生成式人工智能在晶体材料设计领域取得了显著进展，催生了基于图神经网络、扩散模型和大语言模型的方法。现有评估通常遵循稳定性-唯一性-新颖性（S.U.N.）框架，其中稳定性主要使用热力学标准评估，但该标准未能完全涵盖材料实际存在所必需的动力学稳定性。动力学稳定性是决定材料能否被合成并持续存在的关键因素，声子谱计算是其评估标准。然而，此类计算的高昂成本阻碍了生成晶体动力学稳定性的大规模评估。本文中，我们提出PhononBench——首个面向AI生成晶体的动力学稳定性大规模基准测试。借助最新开发的MatterSim原子间势（该势能在超过一万种材料的声子预测中达到密度泛函理论（DFT）级别的精度），PhononBench能够对7个领先晶体生成模型产生的133,838个晶体结构进行高效声子计算与动力学稳定性分析。PhononBench揭示了当前生成模型的普遍局限性：除非特别说明，所有报告的动力学稳定性指标均在-0.1 THz的声子频率阈值下评估，所有生成结构的平均动力学稳定率仅为32.15%，表现最佳的模型MatterGen也仅达到45.05%。此外，我们在-0.001 THz的严格阈值下，识别出32,995个在整个布里渊区声子稳定的晶体结构。同时，可通过网站http://phononbench.cn/访问基于Web的服务，实现分钟级的超快声子预测。