Yuri Cho^1,2, Ruben Laplaza^1,3, Sergi Vela⁴, Clemence Corminboeuf^1,2,3*

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

* Corresponding authors emails: clemence.corminboeuf@epfl.ch

DOI10.24435/materialscloud:zx-t2 [version v1]

Publication date: Nov 15, 2023

How to cite this record

Yuri Cho, Ruben Laplaza, Sergi Vela, Clemence Corminboeuf, Automated prediction of ground state spin for transition metal complexes, Materials Cloud Archive 2023.176 (2023), doi: 10.24435/materialscloud:zx-t2.

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 2,032 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 97% 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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:99c3d6084b126e61fc217c04cdc0f1c2	1.3 KiB	README file explaining the contents of all others.
Ground_state_spin_dataset_chemiscope.json.gz MD5md5:07016a3b1a4e523c51fea2a6d3f4650d Visualize on Chemiscope	1.2 MiB	Ground state spin dataset.
property_2032.txt MD5md5:2ed6d50bad8dd860e4ea2c8c9e0c9891	84.9 KiB	Properties of the 2032 structures in the ground state spin dataset.
refcode_2032.txt MD5md5:26b152df88615422ab83e89a07106598	14.0 KiB	Crystallographic CSD refcodes of the 2032 structures in the ground state spin dataset.
Ground_state_spin_dataset.tar.gz MD5md5:17226f7ed25cb0915ee534923e6cd807	1.4 MiB	XYZ files for the 2032 structures in the ground state spin dataset.
Supplementary_dataset.tar.gz MD5md5:6eb9d9ecd7d6a40be87b802f6daaf1dc	2.4 MiB	XYZ files for the 1850 structures in the ground state spin dataset.
refcode_1850.txt MD5md5:b52a2b50969b321bd3a778b0f52acfa6	12.8 KiB	Crystallographic CSD refcodes of the 1850 structures in the ground state spin dataset.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

spin state transition metal complex transition metal machine learning MARVEL