Unsymmetrical cyanine dyes are widely used in biomolecular detection due to their fluorogenic behavior, whereby fluorescence quantum yields can be very low in fluid solution but are significantly enhanced in conformationally restricted environments. Herein we describe a series of fluorinated analogues of the dye thiazole orange that exhibit improved fluorescence quantum yields and photostabilities. In addition, computational studies on these dyes revealed that twisting about the monomethine bridge beyond an interplanar angle of 60° leads to a dark state that decays nonradiatively to the ground state, accounting for the observed fluorogenic behavior. The effects of position and number of fluorine substituents correlates with both observed quantum yield and calculated activation energy for twisting beyond this critical angle.

We investigate the second-order nonlinear optical properties of octupolar molecules, as opposed to traditional charge transfer dipolar molecules. We consider in particular the case of hexasubstituted aromatic rings, trianiline, trinitrobenzene, and tri-amino-,tri-nitrobenzene (TATB).

Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule. These require only the connectivity of atoms in the molecule as may be obtained from a line diagram or a SMILES string. The second class utilizes the three-dimensional structure of the molecule. These include the Coulomb matrix and Bag of Bonds, which list the inter-atomic distances present in the molecule, and Encoded Bonds, which encode such lists into a feature vector whose length is independent of molecular size. Encoded Bonds' features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules. A wide range of feature sets are constructed by selecting, at each rank, either a graph or geometry-based feature. Here, rank refers to the number of atoms involved in the feature, e.g., atom counts are rank 1, while Encoded Bonds are rank 2. For atomization energies in the QM7 data set, the best graph-based feature set gives a mean absolute error of 3.4 kcal/mol. Inclusion of 3D geometry substantially enhances the performance, with Encoded Bonds giving 2.4 kcal/mol, when used alone, and 1.19 kcal/mol, when combined with graph features.

Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing selfconsistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models. The DFTB layer takes, as input, Hamiltonian matrix elements generated from earlier layers and produces, as output, electronic properties from self-consistent field solutions of the corresponding DFTB Hamiltonian. Backpropagation enables efficient training of the model to target electronic properties. Two types of input to the DFTB layer are explored, splines and feed-forward neural networks. Because overfitting can cause models trained on smaller molecules to perform poorly on larger molecules, regularizations are applied that penalize non-monotonic behavior and deviation of the Hamiltonian matrix elements from those of the published DFTB model used to initialize the model. The approach is evaluated on 15,700 hydrocarbons by comparing the root mean square error in energy and dipole moment, on test molecules with 8 heavy atoms, to the error from the initial DFTB model. When trained on molecules with up to 7 heavy atoms, the spline model reduces the test error in energy by 60% and in dipole moments by 42%. The neural network model performs somewhat better, with error reductions of 67% and 59% respectively. Training on molecules with up to 4 heavy atoms reduces performance, with both the spline and neural net models reducing the test error in energy by about 53% and in dipole by about 25%. arXiv:1808.04526v2 [physics.chem-ph]

The absorption spectra of phenyleneethynylene oligomers show an unusual change in shape with oligomer length. The unusual aspects of the spectra arise from rotation of the phenylene rings about the long axis of the oligomer. In the ground electronic state, the barrier to this rotation is low and the spectra in room temperature come from an ensemble of different structures. In the excited electronic state, the barrier to rotation is substantially higher, giving rise to strong nonlinear electron-phonon coupling. A multidimensional semiempirical model that includes these effects is developed for the photophysics of phenyleneethynylene oligomers. The ground-state energy is modeled with a molecular mechanics expression, and the excitation energy is modeled with an exciton model. Intermediate Neglect of Differential Overlap/Singles Configuration Interaction (INDO/SCI) calculations verify the exciton model and provide initial estimates of the model parameters. These parameters generate the qualitative features seen in experimental spectra. Inclusion of entropy effects from the multiple torsional coordinates is essential. Refinement of the parameters yields quantitative agreement with experiment. This agreement shows that coupling to torsional motion is a major factor in the spectroscopy and photophysics of these conjugated polymers.