新晶体结构隐藏于眼前：AI引导材料发现的压力测试
New Crystal Structures Hide in Plain Sight: A Stress Test for AI-Guided Materials Discovery
AI总结：
通过实验合成发现GdNiSn4和LuNiSn4具有前所未有的结构类型，并以此测试两种生成式AI模型，发现它们无法在容忍度内重现该结构，表明编码化学推理（如已知基元堆叠）是AI发现结构新颖材料的具体路径。
摘要：
新型晶体结构很少被发现，而目前重塑材料发现的人工智能（AI）模型迄今为止只在已知结构家族内产生新的化学成分，而非真正的新结构。我们报道了GdNiSn4和LuNiSn4，这两种金属间化合物采用先前未报道的结构类型，并非通过计算而是通过探索性合成发现。单晶衍射表明该结构是两种已知结构单元的共生体。然后，我们以此系统作为两个领先生成模型MatterGen和DiffCSP++的基准。对于DiffCSP++，基准测试在其晶体学约束设置下进行，使用所需的空间群和Wyckoff位置输入。在我们的采样预算下，两个模型均未在结构匹配容差内恢复实验报道的单斜结构。生成的结构在无需进一步结构弛豫的情况下使用非磁性类似物LuNiSn4进行评估，其中我们排除了4f磁性作为原因。由于新结构由熟悉的构建块构成，它应该是可推导的。我们认为，编码化学推理（例如已知基元的堆叠）是AI能够发现结构新颖材料的具体路径。
英文摘要：
New types of crystal structures are discovered only rarely, and the artificial intelligence (AI) models now reshaping materials discovery have so far produced new chemical compositions within known structural families rather than genuinely new structures. We report GdNiSn4 and LuNiSn4, intermetallics that adopt a previously unreported structure type, found not by computation but by exploratory synthesis. Single-crystal diffraction shows that the structure is an intergrowth of two known structural units. We then use this system as a benchmark for two leading generative models, MatterGen and DiffCSP++. For DiffCSP++, the benchmark is performed in its crystallographically constrained setting, using the required space-group and Wyckoff-position inputs. Under our sampling budget, neither model recovers the experimentally reported monoclinic structure within the structural-matching tolerance. The generated structures are evaluated without further structural relaxation using the nonmagnetic analog LuNiSn4, where we rule out 4f magnetism as the cause. Because the new structure is built from familiar building blocks, it should be derivable. We argue that encoding chemical reasoning, such as the stacking of known motifs, is a concrete path toward AI that can discover structurally novel materials.
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。
