We report combined experimental and theoretical studies of excitation relaxation in poly[2-methoxy,5-(2'-ethyl-hexoxy)-1,4-phenylenevinylene] (MEH-PPV), oligophenylenevinylene (OPV) molecules of varying length, and model PPV chains. We build on the paradigm that the basic characteristics of conjugated polymers are decided by conformational subunits defined by conjugation breaks caused by torsional disorder along the chain. The calculations reported here indicate that for conjugated polymers like those in the PPV family, these conformational subunits electronically couple to neighboring subunits, forming subtly delocalized collective states of nanoscale excitons that determine the polymer optical properties. We find that relaxation among these exciton states can lead to a decay of anisotropy on ultrafast time scales. Unlike in Forster energy transfer, the exciton does not necessarily translate over a large distance. Nonetheless, the disorder in the polymer chain means that even small changes in the exciton size or location has a significant effect on the relaxation pathway and therefore the anisotropy decay.

Electron transfer coupling is a critical factor in determining electron transfer rates. This coupling strength can be sensitive to details in molecular geometries, especially intermolecular configurations. Thus, studying charge transporting behavior with a full first-principle approach demands a large amount of computation resources in quantum chemistry (QC) calculation. To address this issue, we developed a machine learning (ML) approach to evaluate electronic coupling. A prototypical ML model for an ethylene system was built by kernel ridge regression with Coulomb matrix representation. Since the performance of the ML models highly dependent on their building strategies, we systematically investigated the generality of the ML models, the choice of features and target labels. The best ML model trained with 40 000 samples achieved a mean absolute error of 3.5 meV and greater than 98% accuracy in predicting phases. The distance and orientation dependence of electronic coupling was successfully captured. Bypassing QC calculation, the ML model saved 10−10 4 times the computation cost. With the help of ML, reliable charge transport models and mechanisms can be further developed.

We have investigated a single charged microgel in aqueous solution with a combined simulational model and Poisson-Boltzmann theory. In the simulations we use a coarse-grained charged bead-spring model in a dielectric continuum, with explicit counterions and full electrostatic interactions under periodic and nonperiodic boundary conditions. The Poisson-Boltzmann hydrogel model is that of a single charged colloid confined to a spherical cell where the counterions are allowed to enter the uniformly charged sphere. In order to investigate the origin of the differences these two models may give, we performed a variety of simulations of different hydrogel models which were designed to test for the influence of charge correlations, excluded volume interactions, arrangement of charges along the polymer chains, and thermal fluctuations in the chains of the gel. These intermediate models systematically allow us to connect the Poisson-Boltzmann cell model to the bead-spring model hydrogel model in a stepwise manner thereby testing various approximations. Overall, the simulational results of all these hydrogel models are in good agreement, especially for the number of confined counterions within the gel. Our results support the applicability of the Poisson-Boltzmann cell model to study ionic properties of hydrogels under dilute conditions.