The title molecule (C19H17N5O4S·H2O) was synthesized and characterized by IR-NMR spectroscopy, MS and single-crystal X-ray diffraction. The molecular geometry, vibrational frequencies and gauge-independent atomic orbital (GIAO) 1H and 13C NMR chemical shift values of the compound in the ground state have been calculated by using the density functional theory (DFT) method with 6-31G(d) basis set, and compared with the experimental data. All the assignments of the theoretical frequencies were performed by potential energy distributions using VEDA 4 program. The calculated results show that the optimized geometries can well reproduce the crystal structural parameters, and the theoretical vibrational frequencies and 1H and 13C NMR chemical shift values show good agreement with experimental data. To determine conformational flexibility, the molecular energy profile of the title compound was obtained with respect to the selected torsion angle, which was varied from -180° to +180° in steps of 10°. Besides, molecular electrostatic potential (MEP), frontier molecular orbitals (FMO) analysis and thermodynamic properties of the compound were investigated by theoretical calculations.
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http://dx.doi.org/10.1016/j.saa.2011.12.040 | DOI Listing |
Sci Data
January 2025
IBM Research, Hursley, SO21 2JN, UK.
A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA 15213.
The auditory system is unique among sensory systems in its ability to phase lock to and precisely follow very fast cycle-by-cycle fluctuations in the phase of sound-driven cochlear vibrations. Yet, the perceptual role of this temporal fine structure (TFS) code is debated. This fundamental gap is attributable to our inability to experimentally manipulate TFS cues without altering other perceptually relevant cues.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Department of Chemistry, COMSATS University Islamabad, Abbottabad Campus, Abbottabad-22060, Pakistan.
The design and synthesis of nonlinear optical (NLO) materials are rapidly growing fields in optoelectronics. Considering the high demand for newly designed materials with superior optoelectronic characteristics, we investigated the doping process of Group-IIIA elements (namely, B, Al and Ga) onto alkali metal (AM = Li, Na and K)-supported COLi (AM@COLi) complexes to enhance their NLO response. The AM-COLi complexes retained their structural features following interaction with the Group-IIIA elements.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States.
Ice interfaces are pivotal in mediating key chemical and physical processes such as heterogeneous chemical reactions in the environment, ice nucleation, and cloud microphysics. At the ice surface, water molecules form a quasi-liquid layer (QLL) with properties distinct from those of the bulk. Despite numerous experimental and theoretical studies, a molecular-level understanding of the QLL has remained elusive.
View Article and Find Full Text PDFJ Phys Chem Lett
January 2025
State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Calculating anharmonic vibrational modes of molecules for interpreting experimental spectra is one of the most interesting challenges of contemporary computational chemistry. However, the traditional QM methods are costly for this application. Machine learning techniques have emerged as a powerful tool for substituting the traditional QM methods.
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