The remarkable role of steric factors in the reactions of alkynes (HCCH and MeCCMe)with ditungsten hexa-alkoxides: crystal and molecular structures of W2(OPri)6(py)2(µ-C2H2), W2(OCH2But)6(py)2(µ-C2Me2), and W2(OPri)6(µ-C4R4)(C2R2), where R = H and Me (py = pyridine)

Author(s):  
Malcolm H. Chisholm ◽  
Kirsten Folting ◽  
David M. Hoffman ◽  
John C. Huffman ◽  
Joseph Leonelli
2002 ◽  
Vol 35 (25) ◽  
pp. 9420-9425 ◽  
Author(s):  
Peter L. Aldred ◽  
Howard M. Colquhoun ◽  
David J. Williams ◽  
David J. Blundell

1975 ◽  
Vol 53 (19) ◽  
pp. 2930-2943 ◽  
Author(s):  
David F. Rendle ◽  
Alan Storr ◽  
James Trotter

Crystals of the pyrazolylgallium dimethyl dimer (1a) are monoclinic, a = 16.914(3), b = 21.747(6), c = 8.117(2) Å, β = 92.09(3)°, Z = 8, space group P21/c, those of the 3-methyl-pyrazolylgallium dimethyl dimer (1b) are also monoclinic, a = 13.473(6), b = 7.162(2), c = 18.468(4) Å, β = 113.78(2)°, Z = 4, space group P21/c, and those of the indazolylgallium dimethyl dimer (1c) are orthorhombic, a = 8.909(2), b = 27.463(3), c = 15.738(2) Å, Z = 8, space group Pbca. All three structures were solved by Patterson and Fourier methods and were refined by full-matrix least-squares methods to final R values of 0.065, 0.061, and 0.036 for 2542, 2011, and 1986 reflections with I ≥ 3σ(I) respectively. The three crystal structure analyses emphasize the critical role of steric interaction in determining the molecular geometry for these molecules. The results clearly demonstrate that changing deuterium for methyl groups on the gallium atoms and introducing substituents on the bridging 'pyrazolyl' ligands in the [D2Ga·N2C3H3]2 dimer cause a pronounced flattening of the central Ga—(N—N)2—Ga boat conformation and a noticeable lengthening of the Ga—N bonds. Average Ga—N distances are 1.996, 1.986,1.996 Å, and V-angles (the angle between the Ga2N2C3 moieties) are 131.5,139.5, and 147.8° in 1a, 1b, and 1c respectively.


2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.


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