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Ligand optimization of exchange interaction in Co(II) dimer single molecule magnet by machine learning

Sijin Ren1,2,3*, Eric Fonseca2,3, William Perry1,3, Hai-Ping Cheng1,3, Xiao-Guang Zhang1,3, Richard Hennig2,3*

1 Department of Physics, University of Florida, Gainesville, Florida 32611, USA

2 Department of Materials Science and Engineering, University of Florida, Gainesville FL32611, USA

3 Quantum Theory Project, University of Florida, Gainesville FL 32611, USA

* Corresponding authors emails: sijinren@ufl.edu, rhennig@ufl.edu
DOI10.24435/materialscloud:pe-zv [version v1]

Publication date: Dec 07, 2021

How to cite this record

Sijin Ren, Eric Fonseca, William Perry, Hai-Ping Cheng, Xiao-Guang Zhang, Richard Hennig, Ligand optimization of exchange interaction in Co(II) dimer single molecule magnet by machine learning, Materials Cloud Archive 2021.214 (2021), doi: 10.24435/materialscloud:pe-zv.

Description

This record contains data structures used in the manuscript titled Ligand optimization of exchange interaction in Co(II) dimer single molecule magnet by machine learning. Designing single-molecule magnets (SMMs) for potential applications in quantum computing and high-density data storage requires tuning their magnetic properties, especially the strength of the magnetic interaction. These properties can be characterized by first-principles calculation based on density functional theory (DFT). In this work, we study the experimentally synthesized Co(II) dimer SMM with the goal to control the exchange energy between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has a very small exchange energy (< 1meV). We assemble a DFT dataset of 1081 ligand-substitutions for the Co(II) dimer. The ligand exchange provides a broad range of exchange energies from +50 meV to -200 meV, with 80% of the ligands yielding a small exchange energies (<10 meV). We identify descriptors for the classification and regression of exchange energies using gradient boosting machine learning models. We compare structure-based, one-hot encoded, and chemical descriptors consisting of the HOMO/LUMO energies of the individual ligands and the maximum electronegativity difference and bond order for the ligand atom connecting to Co. We observe a similar overall performance with the chemical descriptors outperforming the other descriptors. The record contains: 1) get_data.py: the code and descriptions for loading the data and structures 2) structures_xyz: a folder containing structure files in .xyz format 3) Co_dimer_data.csv and Co_dimer_all.csv: the data files in .csv format

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File name Size Description
Co_dimer_data_all.tar.gz
MD5md5:7d41134d86c9739daf533878a7b4f71b
2.7 MiB This record contains data and structures used in manuscript titled 'Ligand optimization of exchange interaction Co(II) dimer single molecule magnet by machine learning' 1) data is stored in 'Co_dimer_data.csv' and 'Co_dimer_data_all.csv' file, which can be opened in text editors and excel. 2) Optimized structures at both AFM and FM states are stored in 'structures_xyz' folder in .xyz format 3) get_data.py: A python code for loading data and structures

License

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

External references

Journal reference
S. Ren, E. Fonseca, W. Perry, H.-P. Cheng, X.-G. Zhang, R. Hennig, Journal of Physical Chemistry C (Submitted)

Keywords

machine learning Molecular magnet Ligand

Version history:

2021.214 (version v1) [This version] Dec 07, 2021 DOI10.24435/materialscloud:pe-zv