اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدTheoretical characterization of the electronic properties of extended thienylenevinylene oligomers
91
0
0.0
(
0
)
تأليف
Christophe Krzeminski
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
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.
قيم البحث
اقرأ أيضاً
The adsorption of thienylenevinylene oligomers on the Si(100) surface has been investigated using scanning tunneling microscopy. The mode of substitution of the thiophene ring exerts a strong influence on the adsorption configurations and the images
of the oligomer based on 3,4-dihexyl thiophene are highly voltage dependent. We discuss the influence of the alkyl chains on the adsorption process and on the appearance of the molecules in the STM images.
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 p
ropose 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.
Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H$_2$O) and heavy water (D$_2$O) within the isothermal-isobaric ensemble. In particular, the deep neural network is trained based o
n ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water is more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H$_2$O and D$_2$O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.
It was recently discovered that molecular ionization at high x-ray intensity is enhanced, in comparison with that of isolated atoms, through a phenomenon called CREXIM (charge-rearrangement-enhanced x-ray ionization of molecules). X-ray absorption se
lectively ionizes heavy atoms within molecules, triggering electron transfer from neighboring atoms to the heavy atom sites and enabling further ionization there. The present theoretical study demonstrates that the CREXIM effect increases with the size of the molecule, as a consequence of increased intramolecular electron transfer from the larger molecular constituents attached to the heavy atoms. We compare x-ray multiphoton ionization dynamics of xenon, iodomethane, and iodobenzene after interacting with an intense x-ray pulse. Although their photoionization cross sections are similar, iodomethane and iodobenzene molecules are more ionized than xenon atoms. Moreover, we predict that the average total charge of iodobenzene is much larger than that of iodomethane, because of the large number of electrons in the benzene ring. The positive charges transferred from the iodine site to the benzene ring are redistributed such that the higher carbon charges are formed at the far end from the iodine site. Our first-principles calculations provide fundamental insights into the interaction of molecules with x-ray free-electron laser (XFEL) pulses. These insights need to be taken into account for interpreting and designing future XFEL experiments.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
