Published July 1, 2024 | Version v2
Dataset Open

Automated prediction of ground state spin for transition metal complexes

  • 1. Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • 2. National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • 3. National Centre for Competence in Research–Catalysis (NCCR–Catalysis), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • 4. Departament de Ciència de Materials i Química Física and IQTCUB, Universitat de Barcelona, Barcelona, Spain

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Description

Predicting the ground state spin of transition metal complexes is a challenging task. Previous attempts have been focused on specific regions of chemical space, whereas a more general automated approach is required to process crystallographic structures for high-throughput quantum chemistry computations. In this work, we developed a method to predict ground state spins of transition metal complexes. We started by constructing a dataset which contains 2,063 first row transition metal complexes taken from experimental crystal structures and their computed ground state spins. This dataset showed large chemical diversity in terms of metals, metal oxidation states, coordination geometries, and ligands. Then, we analyzed the trends between structural and electronic features of the complexes and their ground state spins, and put forward an empirical spin state assignment model. We also used simple descriptors to build a statistical model with >95% predictive accuracy across the board. With this, we provide a practical and automated way to determine the ground state spin of transition metal complex from structure, enabling the high-throughput exploration of crystallographic repositories.

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References

Journal reference (Paper in which the method and data are discussed.)
Y. Cho, R. Laplaza, S. Vela, C. Corminboeuf, Digital Discovery, 3 (8), 1638–1647 (2024), doi: 10.1039/D4DD00093E

Journal reference (Paper in which the method and data are discussed.)
Y. Cho, R. Laplaza, S. Vela, C. Corminboeuf, Digital Discovery, 3 (8), 1638–1647 (2024)