FUCrIMODo：通过多阶段遗传算法从原子描述符中恢复结构
FUCrIMODo: structure recovery from atomistic descriptors via multi-stage genetic algorithms
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中文总结 AI 辅助
该研究提出一种基于多阶段遗传算法的通用采样方法，可从原子描述符直接恢复原子结构，无需先验结构知识，通过SOAP描述符验证了其能力。
中文摘要 AI 辅助
数据驱动的材料发现方法依赖于原子结构的数值表示作为机器学习模型的输入。将这些描述符逆过程——从其表示中恢复原子结构——对于大多数生成式材料设计流程至关重要，但仍然具有挑战性，尤其是对于周期性系统。现有的逆方法要么针对特定的可逆描述符量身定制，要么需要具有相似原子排列和组成的候选结构，限制了对化学和构型空间中新颖区域的探索。在此，我们提出了一种可泛化的、由相似性驱动的采样方法，该方法由一种新颖的分阶段优化策略提供支持，可直接从描述符中恢复原子类型、原子位置和晶胞形状。我们的方法仅需要描述符特征和参数作为输入，无需任何先验结构知识。我们通过平均原子位置平滑重叠（SOAP）描述符展示了该方法的能力。
英文摘要
Data-driven approaches to materials discovery rely on numerical representations of atomic structures as input for machine learning models. Inverting these descriptors - recovering atomic structures from their representations - is essential for most generative material design pipelines, yet it remains challenging, particularly for periodic systems. Existing inversion methods are either tailored to specific invertible descriptors or require candidate structures with similar atomic arrangements and compositions, limiting the exploration of novel regions in chemical and configurational space. Here, we propose a generalizable, similarity-driven sampling approach, powered by a novel stage-wise optimization strategy, to recover atom types, atomic positions, and unit cell shapes directly from a descriptor. Our approach requires only descriptor features and parameters as input without any prior structural knowledge. The capability of our method is demonstrated by the averaged Smooth Overlap of Atomic Positions (SOAP) descriptor.
