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Machine learning (ML) has shown to advance the research field of quantum chemistry in almost any possible direction and has also recently been applied to investigate the multifaceted photochemistry of molecules. In this paper, we pursue two goals: (i) We show how ML can be used to model permanent dipole moments for excited states and transition dipole moments by adapting the charge model of Gastegger et al. [Chem. Sci. 8, 6924–6935 (2017)], which was originally proposed for the permanent dipole moment vector of the electronic ground state. (ii) We investigate the transferability of our excited-state ML models in chemical space, i.e., whether an ML model can predict the properties of molecules that it has never been trained on and whether it can learn the different excited states of two molecules simultaneously. To this aim, we employ and extend our previously reported SchNarc approach for excited-state ML. We calculate UV absorption spectra from excited-state energies and transition dipole moments as well as electrostatic potentials from latent charges inferred by the ML model of the permanent dipole moment vectors. We train our ML models on C H 2 N H 2 + and C2H4, while predictions are carried out for these molecules and additionally for CHNH2, CH2NH, and C 2 H 5 + . The results indicate that transferability is possible for the excited states. [ABSTRACT FROM AUTHOR]

We regard the rovibrational wave packet dynamics of NaI in a static electric field after femtosecond excitation to its first electronically excited state. The following quasibound nuclear wave packet motion is accompanied by a bonding situation changing from covalent to ionic. At times when the charge separation is present, i.e., when the bond-length is large, a strong dipole moment exists and rotational excitation takes place. Upon bond contraction, the then covalently bound molecule does not experience the external field. This scenario repeats itself periodically. Thus, the vibrational dynamics causes a situation which is comparable to the interaction of the molecule with a train of pulses where the pulse separation is determined by the vibrational period. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

We investigate the combined electronic and nuclear motion in a simple model system as proposed by Shin and Metiu [J. Chem. Phys. 102, 9285 (1995)]. A variation of the electron-nuclei interaction energy allows us to study the transition from an adiabatically uncoupled situation to the case where the adiabatic approximation breaks down. Wave-packet calculations illustrate the dynamical changes of electronic and nuclear probability densities for the case of an adiabatic motion and during a nonadiabatic transition. The influence of the coupling on the transport of electron density between different nuclei is characterized with the help of time-dependent Laplacians. [ABSTRACT FROM AUTHOR]

Accurate excited-state quantum chemical calculations on 2-thiouracil, employing large active spaces and up to quadruple-ζ quality basis sets in multistate complete active space perturbation theory calculations, are reported. The results suggest that the main relaxation path for 2-thiouracil after photoexcitation should be S2 → S1 → T2 → T1, and that this relaxation occurs on a subpicosecond time scale. There are two deactivation pathways from the initially excited bright S2 state to S1, one of which is nearly barrierless and should promote ultrafast internal conversion. After relaxation to the S1 minimum, small singlet-triplet energy gaps and spin-orbit couplings of about 130 cm-1 are expected to facilitate intersystem crossing to T2, from where very fast internal conversion to T1 occurs. An important finding is that 2-thiouracil shows strong pyramidalization at the carbon atom of the thiocarbonyl group in several excited states. [ABSTRACT FROM AUTHOR]

Abstract: We study spectroscopic properties of molecular dimers coupled by dipole–dipole interactions within the framework of time-dependent quantum mechanics. A systematic variation of the dimer geometry allows to establish relationships between the latter and structures in the absorption spectrum. The theoretical model is constructed with the purpose to characterize the changes in absorption and emission properties arising upon aggregation of perylene bisimides. Measured and calculated spectra are compared, thereby addressing the question if a simple exciton model is capable to describe excited state properties of nanoaggregates of these molecules. [Copyright &y& Elsevier]

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system’s geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with an increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133 855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to a significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy. [ABSTRACT FROM AUTHOR]