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:jx-a5 [version v2]

Publication date: Jul 01, 2024

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 2024.100 (2024), https://doi.org/10.24435/materialscloud:jx-a5

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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:9e896e4ac5e402cff4d3da4b7c3b96d0	1.8 KiB	README file explaining the contents of all others.
chemiscopify.ipynb MD5md5:e88088338fb04891868f1ca246ed82e0	15.5 KiB	Jupyter notebook used to generate the chemiscope files from the raw data and structures.
Ground_state_spin_dataset_chemiscope.json.gz MD5md5:0495b07263817f6f824a5204d7c6451e Visualize on Chemiscope	1.2 MiB	Ground state spin dataset.
property_2063.txt MD5md5:48508b9c865985ee3cb41c9180d47445	86.2 KiB	Properties of the 2032 structures in the ground state spin dataset.
refcode_2063.txt MD5md5:e2c49b2edfd4af48c24a07041e7f3a30	14.2 KiB	Crystallographic CSD refcodes of the 2063 structures in the ground state spin dataset.
Ground_state_spin_dataset.tar.gz MD5md5:f22d5ea10de1670772786353a2ae0819	1.5 MiB	XYZ files for the 2063 structures in the ground state spin dataset.
Supplementary_dataset_chemiscope.json.gz MD5md5:4c73cb40f87c41adc36223762f8424ca Visualize on Chemiscope	1.2 MiB	Supplementary ground state spin dataset.
property_1838.txt MD5md5:a8194ca2ebdd46ebee73cafb8ee7d9e9	47.8 KiB	Properties of the 1838 structures in the ground state spin dataset.
refcode_1838.txt MD5md5:08afc6d74a8913fbb7fcbbdd8145abd6	12.7 KiB	Crystallographic CSD refcodes of the 1838 structures in the ground state spin dataset.
Supplementary_dataset.tar.gz MD5md5:3bdc9251ec353f25646e27bac858a949	1.4 MiB	XYZ files for the 1838 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