Capturing Atom-Specific Electronic Structural Dynamics of Transition-Metal Complexes with Ultrafast Soft X-Ray Spectroscopy

2022 ◽  
Vol 73 (1) ◽  
Author(s):  
Raphael M. Jay ◽  
Kristjan Kunnus ◽  
Philippe Wernet ◽  
Kelly J. Gaffney
Keyword(s):  
Electronic Structure ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Iron Pentacarbonyl ◽  
Organometallic Complexes ◽  
Annual Review ◽  
Publication Date ◽  
X Ray ◽  
Metal Ligand

The atomic specificity of X-ray spectroscopies provides a distinct perspective on molecular electronic structure. For 3 d metal coordination and organometallic complexes, the combination of metal- and ligand-specific X-ray spectroscopies directly interrogates metal–ligand covalency—the hybridization of metal and ligand electronic states. Resonant inelastic X-ray scattering (RIXS), the X-ray analog of resonance Raman scattering, provides access to all classes of valence excited states in transition-metal complexes, making it a particularly powerful means of characterizing the valence electronic structure of 3 d metal complexes. Recent advances in X-ray free-electron laser sources have enabled RIXS to be extended to the ultrafast time domain. We review RIXS studies of two archetypical photochemical processes: charge-transfer excitation in ferricyanide and ligand photodissociation in iron pentacarbonyl. These studies demonstrate femtosecond-resolution RIXS can directly characterize the time-evolving electronic structure, including the evolution of the metal–ligand covalency. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

2020 ◽  
Author(s):  
Michael Taylor ◽  
Tzuhsiung Yang ◽  
Sean Lin ◽  
Aditya Nandy ◽  
Jon Paul Janet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Ground State ◽  
Spin States ◽  
Machine Learning Model ◽  
Metal Ligand ◽  
Ligand Bond

<p>Determination of ground-state spins of open-shell transition metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2,000 unique mononuclear Fe(II)/Fe(III) transition metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally-observed temperature-dependent geometric structure changes, by correctly assigning almost all (> 95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model. </p>

scholarly journals Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

2020 ◽  
Author(s):  
Michael Taylor ◽  
Tzuhsiung Yang ◽  
Sean Lin ◽  
Aditya Nandy ◽  
Jon Paul Janet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Ground State ◽  
Spin States ◽  
Machine Learning Model ◽  
Metal Ligand ◽  
Ligand Bond

<p>Determination of ground-state spins of open-shell transition metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2,000 unique mononuclear Fe(II)/Fe(III) transition metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally-observed temperature-dependent geometric structure changes, by correctly assigning almost all (> 95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model. </p>

Preparation, properties and structure of some intermediate nido- and arachno-monocarbaboranes

1981 ◽  
Vol 46 (10) ◽  
pp. 2345-2353 ◽  
Author(s):  
Karel Baše ◽  
Bohumil Štíbr ◽  
Jiří Dolanský ◽  
Josef Duben
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Nmr Spectra ◽  
Liquid Ammonia ◽  
X Ray Diffraction ◽  
X Ray ◽  
H Nmr ◽  
Starting Compound

The 6-N(CH3)3-6-CB9H11 carbaborane reacts with sodium in liquid ammonia with the formation of 6-CB9H12- which was used as a starting compound for preparing the 4-CB8H14, 9-L-6-CB9H13 (L = (CH3)2S, CH3CN and P(C6H5)3), 1-(η5-C5H5)-1,2-FeCB9H10-, and 2,3-(η5-C5H5)2-2,31-Co2CB9H10- carboranes. The 4-CB8H14 compound was dehydrogenated at 623 K to give 4-(7)-CB8H12 carborane. Base degradation of 6-N(CH3)3-6-CB9H11 in methanol resulted in the formation of 3,4-μ-N(CH3)3CH-B5H10. The structure of all compounds was proposed on the basis of their 11B and 1H NMR spectra and X-ray diffraction was used in the case of the transition metal complexes.

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