scholarly journals Spin Crossover in 3D Metal Centers Binding Halide-Containing Ligands: Magnetism, Structure and Computational Studies

2020 ◽  
Vol 12 (6) ◽  
pp. 2512 ◽  
Author(s):  
Paulo N. Martinho ◽  
Frederico F. Martins ◽  
Nuno A. G. Bandeira ◽  
Maria José Calhorda
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Intermolecular Interactions ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
Spin Crossover ◽  
Experimental Studies ◽  
Functional Materials ◽  
Ligand Field ◽  
Other Information

The capability of a given substance to change its spin state by the action of a stimulus, such as a change in temperature, is by itself a very challenging property. Its interest is increased by the potential applications and the need to find sustainable functional materials. 3D transition metal complexes, mainly with octahedral geometry, display this property when coordinated to particular sets of ligands. The prediction of this behavior has been attempted by many authors. It is, however, made very difficult because spin crossover (SCO), as it is called, occurs most often in the solid state, where besides complexes, counter ions, and solvents are also present in many cases. Intermolecular interactions definitely play a major role in SCO. In this review, we decided to analyze SCO in mono- and binuclear transition metal complexes containing halogens as ligands or as substituents of the ligands. The aim was to try and find trends in the properties which might be correlated to halogen substitution patterns. Besides a revision of the properties, we analyzed structures and other information. We also tried to build a simple model to run Density Functional Theory (DFT) calculations and calculate several parameters hoping to find correlations between calculated indices and SCO data. Although there are many experimental studies and single-crystal X-ray diffraction structures, there are only few examples with the F, Cl, Br and series. When their intermolecular interactions were not very different, T1/2 (temperature with 50% high spin and 50% low spin states) usually increased with the calculated ligand field parameter (Δoct) within a given family. A way to predict SCO remains elusive.

scholarly journals Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning

2019 ◽  
Author(s):  
Stefan Gugler ◽  
Jon Paul Janet ◽  
Heather Kulik
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
De Novo ◽  
Functional Materials ◽  
Closed Shell ◽  
Ligand Field ◽  
Structure Property ◽  
Computational Screening

<p>Despite being attractive targets for functional materials, the discovery of transition metal complexes with high-throughput computational screening is challenged by the amount of feasible coordination numbers, spin states, or oxidation states and the potentially large sizes of ligands. To overcome these limitations, we take inspiration from organic chemistry where full enumeration of neutral, closed shell molecules under the constraint of size has enriched discovery efforts. We design monodentate and bidentate ligands from scratch for the construction of mononuclear, octahedral transition metal complexes with up to 13 heavy atoms (i.e., metal, C, N, O, P, or S). From > 11,000 theoretical ligands, we develop a heuristic score for ranking a chemically feasible 2,500 ligand subset, only 71 of which were previously included in common organic molecule databases. We characterize the top 20% of scored ligands with density functional theory (DFT) in an octahedral homoleptic ligand database (OHLDB). The OHLDB contains i) the geometry optimized structures of 1,250 homoleptic octahedral complexes obtained from the enumerated pool of ligands and an open-shell transition metal (M(II)/M(III), M = Cr, Mn, Fe, or Co), and ii) the resulting high-spin/low-spin adiabatic electronic energies (<b>Δ</b><i>E</i><sub>H-L</sub>) obtained with hybrid DFT. Over the OHLDB, we observe structure–property (i.e., <b>Δ</b><i>E</i><sub>H-L</sub>) relationships different from those expected on the basis of ligand field arguments or from our prior data sets. Finally, we demonstrate how incorporating OHLDB data into artificial neural network (ANN) training improves ANN out-of-sample performance on much larger transition metal complexes.</p>

scholarly journals Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning

2019 ◽  
Author(s):  
Stefan Gugler ◽  
Jon Paul Janet ◽  
Heather Kulik
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
De Novo ◽  
Functional Materials ◽  
Closed Shell ◽  
Ligand Field ◽  
Structure Property ◽  
Computational Screening

<p>Despite being attractive targets for functional materials, the discovery of transition metal complexes with high-throughput computational screening is challenged by the amount of feasible coordination numbers, spin states, or oxidation states and the potentially large sizes of ligands. To overcome these limitations, we take inspiration from organic chemistry where full enumeration of neutral, closed shell molecules under the constraint of size has enriched discovery efforts. We design monodentate and bidentate ligands from scratch for the construction of mononuclear, octahedral transition metal complexes with up to 13 heavy atoms (i.e., metal, C, N, O, P, or S). From > 11,000 theoretical ligands, we develop a heuristic score for ranking a chemically feasible 2,500 ligand subset, only 71 of which were previously included in common organic molecule databases. We characterize the top 20% of scored ligands with density functional theory (DFT) in an octahedral homoleptic ligand database (OHLDB). The OHLDB contains i) the geometry optimized structures of 1,250 homoleptic octahedral complexes obtained from the enumerated pool of ligands and an open-shell transition metal (M(II)/M(III), M = Cr, Mn, Fe, or Co), and ii) the resulting high-spin/low-spin adiabatic electronic energies (<b>Δ</b><i>E</i><sub>H-L</sub>) obtained with hybrid DFT. Over the OHLDB, we observe structure–property (i.e., <b>Δ</b><i>E</i><sub>H-L</sub>) relationships different from those expected on the basis of ligand field arguments or from our prior data sets. Finally, we demonstrate how incorporating OHLDB data into artificial neural network (ANN) training improves ANN out-of-sample performance on much larger transition metal complexes.</p>

Density Functional Calculations of EPR Parameter g Tensors of Some Transition Metal Complexes

2011 ◽  
Vol 2 (2) ◽  
pp. 139-141
Author(s):  
Vinita Prajapati ◽  
◽  
P.L.Verma P.L.Verma ◽  
Dhirendra Prajapati ◽  
B.K.Gupta B.K.Gupta
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional Calculations ◽  
Density Functional

scholarly journals 1,3,5-Triaza-7-Phosphaadamantane (PTA) as a 31P NMR Probe for Organometallic Transition Metal Complexes in Solution

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1390 ◽  
Author(s):  
Ilya G. Shenderovich
Keyword(s):  
Chemical Shift ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
Molecular Structures ◽  
Basis Sets ◽  
Functional Theory ◽  
Non Covalent Interactions ◽  
Covalent Interactions

Due to the rigid structure of 1,3,5-triaza-7-phosphaadamantane (PTA), its 31P chemical shift solely depends on non-covalent interactions in which the molecule is involved. The maximum range of change caused by the most common of these, hydrogen bonding, is only 6 ppm, because the active site is one of the PTA nitrogen atoms. In contrast, when the PTA phosphorus atom is coordinated to a metal, the range of change exceeds 100 ppm. This feature can be used to support or reject specific structural models of organometallic transition metal complexes in solution by comparing the experimental and Density Functional Theory (DFT) calculated values of this 31P chemical shift. This approach has been tested on a variety of the metals of groups 8–12 and molecular structures. General recommendations for appropriate basis sets are reported.

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