Dataset Information

Magnetic Properties of Metal?Organic Coordination Networks Based on 3d Transition Metal Atoms.

ABSTRACT: The magnetic anisotropy and exchange coupling between spins localized at the positions of 3d transition metal atoms forming two-dimensional metal?organic coordination networks (MOCNs) grown on a Au(111) metal surface are studied. In particular, we consider MOCNs made of Ni or Mn metal centers linked by 7,7,8,8-tetracyanoquinodimethane (TCNQ) organic ligands, which form rectangular networks with 1:1 stoichiometry. Based on the analysis of X-ray magnetic circular dichroism (XMCD) data taken at T = 2.5 K, we find that Ni atoms in the Ni?TCNQ MOCNs are coupled ferromagnetically and do not show any significant magnetic anisotropy, while Mn atoms in the Mn?TCNQ MOCNs are coupled antiferromagnetically and do show a weak magnetic anisotropy with in-plane magnetization. We explain these observations using both a model Hamiltonian based on mean-field Weiss theory and density functional theory calculations that include spin?orbit coupling. Our main conclusion is that the antiferromagnetic coupling between Mn spins and the in-plane magnetization of the Mn spins can be explained by neglecting effects due to the presence of the Au(111) surface, while for Ni?TCNQ the metal surface plays a role in determining the absence of magnetic anisotropy in the system.

SUBMITTER: Blanco-Rey M

PROVIDER: S-EPMC6017066 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Magnetic Properties of Metal⁻Organic Coordination Networks Based on 3d Transition Metal Atoms.

Blanco-Rey María M Sarasola Ane A Nistor Corneliu C Persichetti Luca L Stamm Christian C Piamonteze Cinthia C Gambardella Pietro P Stepanow Sebastian S Otrokov Mikhail M MM Golovach Vitaly N VN Arnau Andres A

Molecules (Basel, Switzerland) 20180420 4

The magnetic anisotropy and exchange coupling between spins localized at the positions of 3d transition metal atoms forming two-dimensional metal⁻organic coordination networks (MOCNs) grown on a Au(111) metal surface are studied. In particular, we consider MOCNs made of Ni or Mn metal centers linked by 7,7,8,8-tetracyanoquinodimethane (TCNQ) organic ligands, which form rectangular networks with 1:1 stoichiometry. Based on the analysis of X-ray magnetic circular dichroism (XMCD) data taken at T = ...[more]

PMID: 29677142

Similar Datasets

Project description:In recent studies, porphyrin derivatives have been frequently used as building blocks for the fabrication of metal-organic coordination networks (MOCNs) on metal surfaces under ultrahigh vacuum conditions (UHV). The porphyrin core can host a variety of 3d transition metals, which are usually incorporated in solution. However, the replacement of a pre-existing metal atom in the porphyrin core by a different metallic species has been rarely reported under UHV. Herein, we studied the influence of cyanophenyl and pyridyl functional endgroups in the self-assembly of structurally different porphyrin-based MOCNs by the deposition of Fe atoms on tetracyanophenyl (Co-TCNPP) and tetrapyridyl-functionalized (Zn-TPPyP) porphyrins on Au(111) by means of scanning tunneling microscopy (STM). A comparative analysis of the influence of the cyano and pyridyl endgroups on the formation of different in-plane coordination motifs is performed. Each porphyrin derivative formed two structurally different Fe-coordinated MOCNs stabilized by three- and fourfold in-plane coordination nodes, respectively. Interestingly, the codeposited Fe atoms did not only bind to the functional endgroups but also reacted with the porphyrin core of the Zn-substituted porphyrin (Zn-TPyP), i.e., an atom exchange reaction took place in the porphyrin core where the codeposited Fe atoms replaced the Zn atoms. This was evidenced by the appearance of molecules with an enhanced (centered) STM contrast compared with the appearance of Zn-TPyP, which suggested the formation of a new molecular species, i.e., Fe-TPPyP. Furthermore, the porphyrin core of the Co-substituted porphyrin (Co-TCNPP) displayed an off-centered STM contrast after the deposition of Fe atoms, which was attributed to the binding of the Fe atoms on the top site of the Co-substituted porphyrin core. In summary, the deposition of metal atoms onto organic layers can steer the formation of structurally different MOCNs and may replace pre-existing metal atoms contained in the porphyrin core.

Project description:High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe subtle trends in inorganic bonding and spin-state ordering, but these calculations are computationally costly and properties are sensitive to the exchange-correlation functional employed. To begin to overcome these challenges, we trained artificial neural networks (ANNs) to predict quantum-mechanically-derived properties, including spin-state ordering, sensitivity to Hartree-Fock exchange, and spin-state specific bond lengths in transition metal complexes. Our ANN is trained on a small set of inorganic-chemistry-appropriate empirical inputs that are both maximally transferable and do not require precise three-dimensional structural information for prediction. Using these descriptors, our ANN predicts spin-state splittings of single-site transition metal complexes (i.e., Cr-Ni) at arbitrary amounts of Hartree-Fock exchange to within 3 kcal mol-1 accuracy of DFT calculations. Our exchange-sensitivity ANN enables improved predictions on a diverse test set of experimentally-characterized transition metal complexes by extrapolation from semi-local DFT to hybrid DFT. The ANN also outperforms other machine learning models (i.e., support vector regression and kernel ridge regression), demonstrating particularly improved performance in transferability, as measured by prediction errors on the diverse test set. We establish the value of new uncertainty quantification tools to estimate ANN prediction uncertainty in computational chemistry, and we provide additional heuristics for identification of when a compound of interest is likely to be poorly predicted by the ANN. The ANNs developed in this work provide a strategy for screening transition metal complexes both with direct ANN prediction and with improved structure generation for validation with first principles simulation.

Dataset Information

Magnetic Properties of Metal?Organic Coordination Networks Based on 3d Transition Metal Atoms.

Publications

Magnetic Properties of Metal⁻Organic Coordination Networks Based on 3d Transition Metal Atoms.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure