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We introduce a novel statistical mechanics-based analysis of electroactive copolymer networks. This enables to express the coupled response of a multi-species polymer network in terms of a Gibbs-like energy-density function. Following the common affine deformation assumption we derive the expressions for the network response in terms of the species' properties and molar fraction. Next, we specialize the results to the class of monomers with spontaneous electric dipoles and determine the monomers' orientational distribution in terms of a generalized Langevin function. We reveal that the coupled response of the copolymer depends on the contrast between the intensities of the dipolar moments of the species and their fraction of the chain end-to-end length. In the long chains limit we obtain closed-form expressions for the electrical and mechanical fields. Next, we consider two-species copolymers and find that the long chains approximation is valid for actuation stretch ratios up to 40% of the locking stretch. We also demonstrate that the maximal susceptibility is attained when the contour lengths of the two species are equal. Moreover, the overall electroelastic response is enhanced if the monomers with the weaker dipole are longer than those with the stronger one. Finally, we show that an appropriate choice of actual monomers may lead to copolymers with relative dielectric constants of more than 30. [ABSTRACT FROM AUTHOR]

We present parallel version of Rosenbluth Self-Avoiding Walk generation method implemented on Graphics Processing Units (GPUs) using CUDA libraries. The method scales almost linearly with the number of CUDA cores and the method efficiency has only hardware limitations. The method is introduced in two realizations: on a cubic lattice and in real space. We find a good agreement between serial and parallel implementations and consistent results between lattice and real space realizations of the method for linear chain statistics. The developed GPU implementations of Rosenbluth algorithm can be used in Monte Carlo simulations and other computational methods that require large sampling of molecules conformations. [ABSTRACT FROM AUTHOR]