AI 中文总结
该研究提出一种元素映射旋转不变描述符方法，结合可训练块对角投影与MLP，实现272类晶体结构原型的原子级分类，准确率达99.38%，运行高效可用于网站部署。
AI 中文摘要
我们提出了一种从局部原子环境中识别晶体结构原型的方法。将中心原子及其距离归一化截断范围内的近邻原子按邻近度映射为匿名元素类型（A/B/C/D），并针对每种类型块计算NEP风格的描述符——径向切比雪夫矩加收缩球谐（S-vector）不变量。该描述符通过构造实现旋转不变性（采用带有解析归一化常数的MLFF风格收缩），因此无需进行旋转增强。一个可训练的块对角投影将316维原始基压缩为168维学习得到的描述符，输入到三层多层感知机（MLP）中。该模型将每个原子分类为271种AFLOW晶体结构原型或非晶态类别（共272类），在538190个原子级样本上达到99.38%的准确率。完整流程（描述符提取+推理）在单个CPU核心上运行，每10000个原子耗时3.9秒，适合网站部署。
英文摘要
We present a method for identifying crystal structure prototypes from local atomic environments. A center atom and its neighbors within a distance-normalized cutoff are mapped to anonymous element types (A/B/C/D) by proximity, and a NEP-style descriptor-radial Chebyshev moments plus contracted spherical-harmonic (S-vector) invariants-is computed per type block. The descriptor is rotation-invariant by construction (MLFF-style contraction with analytic normalization constants), so no rotation augmentation is needed. A trainable block-diagonal projection compresses the 316-dimensional raw basis into a 168-dimensional learned descriptor fed to a three-layer MLP. The model classifies each atom into one of 271 AFLOW crystal structure prototypes or the amorphous class (272 classes), reaching 99.38% accuracy on 538,190 per-atom samples. The full pipeline (descriptor extraction + inference) runs on a single CPU core at 3.9 s per 10,000 atoms, making it suitable for website deployment.
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