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Register a new userTheoretical characterization of the electronic properties of extended thienylenevinylene oligomers
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Added by
Christophe Krzeminski
Publication date
2011
fields
Physics
and research's language is
English
Authors
Christophe Krzeminski
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We present semiempirical tight binding calculations on thienylenevinylene oligomers up to the hexadecamer stage (n=16) and ab initio calculations based on the local density approximation up to n=8. The results correctly describe the experimental variations of the gap versus size, the optical spectra, and the electrochemical redox potentials. We propose a simple model to deduce from the band structure of the polymer chain the electronic states of the oligomers close to the gap. We analyze the evolution of the gap as a function of the torsion angle between consecutive cells: the modifications are found to be small up to a ~30^{circ}; angle. We show that these oligomers possess extensive pi-electron delocalization along the molecular backbone which makes them interesting for future electronic applications such as molecular wires.
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Combining two-color infared pump-probe spectroscopy and anharmonic force field calculations we characterize the anharmonic coupling patterns between fingerprint modes and the hydrogen-bonded symmetric NH$_2$ stretching vibration in adenine-thymine dA$_{20}$-dT$_{20}$ DNA oligomers. Specifically, it is shown that the anharmonic coupling between the NH$_2$ bending and the CO stretching vibration, both absorbing around 1665 cm-1, can be used to assign the NH$_2$ fundamental transition at 3215 cm-1 despite the broad background absorption of water.
Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties, in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water, and transition dipole moment of a model structural unit of proteins. Machine learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials.
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