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Published on: June 9, 2023
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Automated prediction of ground state spin for transition metal complexes
Yuri Cho1,2, Ruben Laplaza1,3, Sergi Vela4,5
1Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne Lausanne Switzerland clemence.corminboeuf@epfl.ch.
Digital Discovery
|August 9, 2024
Summary
We developed a method to determine the ground state spin of transition metal complexes directly from crystal structures. This enables large-scale quantum chemical computations using crystallographic data.
Area of Science:
- Crystallography
- Quantum Chemistry
- Computational Materials Science
Background:
- Extracting molecular structure, charge, and spin from crystallographic data is crucial for large-scale quantum chemical computations.
- Previous work focused on oxidation states and bond order within the cell2mol software.
Purpose of the Study:
- To develop a general approach for assigning the ground state spin of transition metal complexes.
- To enable automated use of crystallographic data for computations involving transition metal complexes.
Main Methods:
- Constructed the TM-GSspin dataset from 31k transition metal complexes in the Cambridge Structural Database using cell2mol.
- Analyzed correlations between structural/electronic features and ground state spins.
- Developed a rule-based spin state assignment model and a statistical prediction model.
Main Results:
- The TM-GSspin dataset contains 2063 mononuclear first-row transition metal complexes with computed ground state spins.
- Achieved 98% cross-validated accuracy in predicting ground state spin.
- Identified key correlations between complex features and spin states.
Conclusions:
- The developed approach accurately predicts ground state spin directly from crystal structures.
- This method facilitates large-scale quantum chemical computations by automating spin determination.
- Enhances the utility of crystallographic data for transition metal complex research.
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