从启发式到机器学习：单离子磁体的性能上限及其电子起源
From Heuristics to Machine Learning: The Performance Ceiling for Single-Ion Magnets and Its Electronic Origin
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中文总结 AI 辅助
本研究通过比较三种结构描述层级，发现机器学习预测单离子磁体的准确率上限约为76%，并揭示几何方法无法捕捉隧穿和集体弛豫等电子起源，提出结合简单过滤器与从头计算描述符的策略。
中文摘要 AI 辅助
机器学习（ML）有望加速单离子磁体（SIMs）的发现，但合成前可用的结构信息是否足以支持此类预测？针对SIMDAVIS 1.2.1数据库中的1215个镧系配合物，我们比较了三种递增的结构描述层级：配位位点的表格化特征、配位多面体的连续对称性度量，以及原子的完整三维排列。三者均收敛至接近76%的准确率，仅略高于单一规则“对Dy3+预测为SIM”的71%。为解释失败原因，我们将多参考从头计算方法与高置信度错误背后结构的检查相结合。被几何模型遗漏的SIMs是场诱导弛豫体，其基态Kramers双重态易发生隧穿，这一性质对几何描述符不可见。许多假阳性包含多个镧系中心或自由基，因此其弛豫是集体性的，超出了单离子图像。识别SIMs所需的电子结构和连通性信息因此无法仅通过几何方法获得。几何模型仍然有用：将筛选限制在高预测置信度的化合物上，准确率提升至88%，同时保留了数据集的48%。基于对失败的分析，我们提出了一种策略，将核数和自由基的简单过滤器与从头计算得到的配体场描述符相结合。
英文摘要
Machine learning (ML) is expected to speed up the discovery of single-ion magnets (SIMs), but does the structural information available before synthesis allow such predictions? For 1215 lanthanide complexes from the SIMDAVIS 1.2.1 database we compared three increasing levels of structural description: tabular features of the coordination site, continuous symmetry measures of the coordination polyhedron, and the complete 3D arrangement of atoms. All three converge to an accuracy near 76%, only slightly above the 71% of the single rule "predict SIM for Dy3+". To explain the failures, we combined multireference ab initio calculations with an inspection of the structures behind the high-confidence errors. The SIMs missed by the geometric models are field-induced relaxers whose ground Kramers doublets are prone to tunnelling, a property invisible to geometric descriptors. Many false positives contain several lanthanide centers or radicals, so their relaxation is collective and outside the single-ion picture. The electronic-structure and connectivity information needed to identify SIMs is therefore not accessible to geometric methods alone. Geometric models remain useful: restricting the screening to compounds with high prediction confidence raises the accuracy to 88% while retaining 48% of the dataset. Building on the analysis of the failures, we propose a strategy that combines simple filters for nuclearity and for radicals with ligand-field descriptors from ab initio calculations.
