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<p>This study explores the coordination geometry of tetrachlorocuprate(II) anions (CuCl₄²⁻) in the solid state using data mining techniques applied to crystallographic databases. By analyzing over 800 crystal structures from the Cambridge Structural Database, the research reveals that the commonly assumed square planar and tetrahedral geometries are underrepresented. Instead, a disphenoidal (flattened tetrahedral) geometry emerges as the predominant configuration. Principal Component Analysis (PCA) of six Cl–Cu–Cl bond angles confirms a continuous deformation pathway between idealized geometries, mapping a pseudorotation. Comparative studies with related halocuprates and other metal ions (Ni²⁺, Zn²⁺) further support the influence of electronic configuration and ligand repulsion on geometry preferences. The work also serves as an educational case study, demonstrating how database searches and statistical tools can enhance structural chemistry literacy and prepare future researchers for intelligent use of Artificial Intelligence (AI) in chemical data analysis.</p>

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A simple exercise of structure correlation to investigate tetrachlorocuprate geometric preferences and discuss data mining literacy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Alessia Bacchi

摘要

This study explores the coordination geometry of tetrachlorocuprate(II) anions (CuCl₄²⁻) in the solid state using data mining techniques applied to crystallographic databases. By analyzing over 800 crystal structures from the Cambridge Structural Database, the research reveals that the commonly assumed square planar and tetrahedral geometries are underrepresented. Instead, a disphenoidal (flattened tetrahedral) geometry emerges as the predominant configuration. Principal Component Analysis (PCA) of six Cl–Cu–Cl bond angles confirms a continuous deformation pathway between idealized geometries, mapping a pseudorotation. Comparative studies with related halocuprates and other metal ions (Ni²⁺, Zn²⁺) further support the influence of electronic configuration and ligand repulsion on geometry preferences. The work also serves as an educational case study, demonstrating how database searches and statistical tools can enhance structural chemistry literacy and prepare future researchers for intelligent use of Artificial Intelligence (AI) in chemical data analysis.