The conformational energy landscape and the associated electronic structure and spectroscopic properties (UV/Vis/near-infrared (NIR) and IR) of three formally d(5)/d(6) mixed-valence diruthenium complex cations, [{Ru(dppe)Cp*}2(μ-C≡CC6H4C≡C)](+), [1](+), [trans-{RuCl(dppe)2}2(μ-C≡CC6H4C≡C)](+), [2](+), and the Creutz-Taube ion, [{Ru(NH3)5}2(μ-pz)](5+), [3](5+) (Cp = cyclopentadienyl; dppe = 1,2-bis(diphenylphosphino)ethane; pz = pyrazine), have been studied using a nonstandard hybrid density functional BLYP35 with 35 % exact exchange and continuum solvent models. For the closely related monocations [1](+) and [2](+), the calculations indicated that the lowest-energy conformers exhibited delocalized electronic structures (or class III mixed-valence character). However, these minima alone explained neither the presence of shoulder(s) in the NIR absorption envelope nor the presence of features in the observed vibrational spectra characteristic of both delocalized and valence-trapped electronic structures. A series of computational models have been used to demonstrate that the mutual conformation of the metal fragments--and even more importantly the orientation of the bridging ligand relative to those metal centers--influences the electronic coupling sufficiently to afford valence-trapped conformations, which are of sufficiently low energy to be thermally populated. Areas in the conformational phase space with variable degrees of symmetry breaking of structures and spin-density distributions are shown to be responsible for the characteristic spectroscopic features of these two complexes. The Creutz-Taube ion [3](5+) also exhibits low-lying valence-trapped conformational areas, but the electronic transitions that characterize these conformations with valence-localized electronic structures have low intensities and do not influence the observed spectroscopic characteristics to any notable extent.
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Sci Rep
December 2024
Department of Electrical and Electronics Engineering, Engineering Faculty, Düzce University, Düzce, Turkey.
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December 2024
Department of Physics, Indian Institute of Technology, Patna, 801106, Bihar, India.
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December 2024
Key Laboratory for Quantum Materials of Zhejiang Province, Department of Physics, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang Province, China.
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December 2024
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.
Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology with graph neural network which offers an accuracy of and an F1 score of in classifying topological versus non-topological materials, outperforming the other state-of-the-art classifier models.
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Institute of Physics, Chinese Academy of Sciences, Beijing, China.
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