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Conjugated polymers consist of complex backbone structures and side‐chain moieties to meet various optoelectronic and processing requirements. Recent work on conjugated polymers has been devoted to studying the mechanical properties and developing new conjugated polymers with low modulus and high‐crack onset strain, while the thin film mechanical stability under long‐term external tensile strain is less investigated. Here we performed direct mechanical stress relaxation tests for both free‐standing and thin film floated on water surface on both high‐T_gand low‐T_gconjugated polymers, as well as a reference nonconjugated sample, polystyrene. We measured thin films with a range of film thickness from 38 to 179 nm to study the temperature and thickness effect on thin film relaxation, where an apparent enthalpy–entropy compensation effect for glassy polymer PS and PM6 thin films was observed. We also compared relaxation times across three different conjugated polymers and showed that both crystalline morphology and higher modulus reduce the relaxation rate besides higher glass transition temperature. Our work provides insights into the mechanical creep behavior of conjugated polymers, which will have an impact on the future design of stable functional organic electronics.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed computational miniature mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model’s generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

We introduce an approach to treat localized correlated electronic states in the otherwise weakly correlated host medium. Here, the environment is dynamically downfolded on the correlated subspace. It is captured via renormalization of one and two quasiparticle interaction terms which are evaluated using many-body perturbation theory. We outline the strategy on how to take the dynamical effects into account by going beyond the static limit approximation. Further, we introduce an efficient stochastic implementation that enables treating the host environment with a large number of electrons at a minimal computational cost. For a small explicitly correlated subspace, the dynamical effects are critical. We demonstrate the methodology by reproducing optical excitations in the negatively charged NV center defect in diamond, that agree with experimental results.

The vertex function (Γ) within the Green’s function formalism encapsulates information about all higher-order electron–electron interaction beyond those mediated by density fluctuations. Herein, we present an efficient approach that embeds vertex corrections in the one-shot GW correlation self-energy for isolated and periodic systems. The vertex-corrected self-energy is constructed through the proposed separation–propagation–recombination procedure: the electronic Hilbert space is separated into an active space and its orthogonal complement denoted as the “rest;” the active component is propagated by a space-specific effective Hamiltonian different from the rest. The vertex corrections are introduced by a rescaled time-dependent nonlocal exchange interaction. The direct Γ correction to the self-energy is further updated by adjusting the rescaling factor in a self-consistent post-processing cycle. Our embedding method is tested mainly on donor–acceptor charge-transfer systems. The embedded vertex effects consistently and significantly correct the quasiparticle energies of the gap-edge states. The fundamental gap is generally improved by 1–3 eV upon the one-shot GW approximation. Furthermore, we provide an outlook for applications of (embedded) vertex corrections in calculations of extended solids.