PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability

AI 中文解读

1. 核心亮点：科学家发布了一套名为PhononBench-MP40的“材料体检”数据集，让计算机能像医生看X光片一样，快速判断一种新材料是否真正稳定可靠。 2. 通俗解读：在研发新材料时，计算机常常通过模拟来预测候选材料的性能。但有个棘手问题：有些材料理论上结构完美，实际模拟中却会出现“虚频”——就像建筑模型在某些地方会轻微晃动，意味着材料其实不稳定。PhononBench-MP40数据集就像一个收录了近4.7万份材料的“振动档案库”，每份材料都附上了详细的振动频谱和稳定性标签。研究人员可以拿着这些“体检报告”训练AI模型，让计算机学会自主判断哪些材料是真正有潜力的“好苗子”，哪些只是“纸老虎”。 3. 实际影响：虽然普通人不会直接使用这个数据集，但它将大大加速新材料的筛选过程。比如，未来开发更轻便的手机外壳、更耐用的电动汽车电池、更高效的太阳能板时，工程师们能利用这个工具更精准地避开那些看似完美实则不稳定的材料，从而节省大量实验时间和成本。最终，我们将更快用上性能更好、更安全的新型材料产品。

arXiv:2607.22573v1 Announce Type: new Abstract: Imaginary phonon modes remain a practical bottleneck in computational materials screening because otherwise plausible structures can be locally dynamically unstable under a chosen workflow. Here we present PhononBench-MP40, a spectrum-resolved benchmark dataset of Materials Project-derived crystals for workflow-defined phonon stability. The dataset starts from 47,969 MP40 workflow tasks and provides 46,899 completed records with paired stability labels and local phonopy YAML spectra, including 16,683 Stable records and 30,216 completed-phonon unstable records. A further 1,067 relaxation failures are reported separately rather than merged into the completed phonon denominator. The release centers on the local YAML spectrum: the stability label, the lowest sampled frequency and any threshold-dependent relabeling are derived from that spectrum. The dataset is openly available through Science Data Bank at https://doi.org/10.57760/sciencedb.38735. A companion GitHub repository provides the calculation code and lightweight access utilities. PhononBench-MP40 provides an auditable reference for workflow-defined stability classification, minimum-frequency analysis, threshold studies and failure-aware triage, while keeping the reference workflow, data schema and interpretation boundaries explicit.