Methyl-p-benzoquinone (MpBQ, CH3C6H3(═O)2) is a prototypical molecule in the study of quinones, which are compounds of relevance in biology and several redox reactions. Understanding the electron attachment properties of MpBQ and its ability to form anions is crucial in elucidating its role in these reactions. In this study, we investigate electron attachment to MpBQ employing a crossed electron-molecular beam experiment in the electron energy range of approximately 0 to 12 eV, as well as theoretical approaches using quantum chemical and electron scattering calculations. Six anionic species were identified: C7H6O2–, C7H5O2–, C6H5O–, C4HO–, C2H2–, and O–. The parent anion is formed most efficiently, with large cross sections, through two resonances at electron energies between 1 and 2 eV. Potential reaction pathways for all negative ions observed are explored, and the experimental appearance energies are compared with calculated thermochemical thresholds. Although exhibiting similar electron attachment properties to pBQ, MpBQ’s additional methyl group introduces entirely new dissociative reactions, while quenching others, underscoring its distinctive chemical behavior.

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To expand the QUEST database of highly accurate vertical transition energies, we consider a series of large organic chromogens ubiquitous in dye chemistry, such as anthraquinone, azobenzene, BODIPY, and naphthalimide. We compute, at the CC3 level of theory, the singlet and triplet vertical transition energies associated with the low-lying excited states. This leads to a collection of more than 120 new highly accurate excitation energies. For several singlet transitions, we have been able to determine CCSDT transition energies with a compact basis set, finding minimal deviations from the CC3 values for most states. Subsequently, we employ these reference values to benchmark a series of lower-order wave function approaches, including the popular ADC(2) and CC2 schemes, as well as time-dependent density-functional theory (TD-DFT), both with and without applying the Tamm–Dancoff approximation (TDA). At the TD-DFT level, we evaluate a large panel of global, range-separated, local, and double hybrid functionals. Additionally, we assess the performance of the Bethe–Salpeter equation (BSE) formalism relying on both G0W0 and evGW quasiparticle energies evaluated from various starting points. It turns out that CC2 and ADC(2.5) are the most accurate models among those with respective O(N5) and O(N6) scalings with system size. In contrast, CCSD does not outperform CC2. The best performing exchange–correlation functionals include BMK, M06–2X, M06-SX, CAM-B3LYP, ωB97X-D, and LH20t, with average deviations of approximately 0.20 eV or slightly below. Errors on vertical excitation energies can be further reduced by considering double hybrids. Both SOS-ωB88PP86 and SOS-ωPBEPP86 exhibit particularly attractive performances with overall quality on par with CC2, whereas PBE0-DH and PBE-QIDH are only slightly less efficient. BSE/evGW calculations based on Kohn–Sham starting points have been found to be particularly effective for singlet transitions, but much less for their triplet counterparts.

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This article follows earlier studies on the physical origin of magnetic anisotropy and the means of controlling it in polynuclear transition metal complexes. The difficulties encountered when focusing a magnetic field on a molecular object have led to consider the electric field as a more appropriate control tool. It is therefore fundamental to understand what governs the sensitivity of magnetic properties to the application of an electric field. We have already studied the impact of the electric field on the isotropic exchange coupling and on the Dzyaloshinskii–Moriya interaction (DMI). Here, we focus on the symmetric exchange anisotropy tensor. In order to obtain significant values of anisotropic interactions, we have carried out this study on a model complex that exhibits first-order spin–orbit coupling. We will show that (i) large values of the axial parameter of symmetric exchange can be reached when close to the first-order spin–orbit coupling regime, (ii) both correlated energies and wave functions must be used to achieve accurate values of the symmetric tensor components when the DMI is non-zero, and (iii) finally, an interferential effect between the DMI and the axial parameter of symmetric exchange occurs for a certain orientation of the electric field, i.e., the latter decreases in magnitude as the former increases. While DMI is often invoked as being involved in magneto-electric coupling, isotropic exchange and the symmetrical anisotropic tensor also contribute. Finally, we provide a recipe for generating significant anisotropic interactions and a significant change in magnetic properties under an electric field.

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Silicon nanostructures have a rich optical response thanks to Mie-type optical resonances, that can be designed on-demand via their geometry. It is possible to encode bits of information in a nanostructure’s geometry, and retrieve this information optically via the color observed in dark-field microscopy. Furthermore, asymmetric structures can profit from the illuminating light polarization to facilitate information readout. Our ultimate goal is to accurately reverse engineer experimentally feasible silicon nanostructures for information encoding, such that they implement a set of ideally distinguishable colors for robust optical readout. Deep learning is increasingly being used to solve inverse problems such as nano-photonic structure design. Neural networks for inverse design are mostly trained on simulated data, which is cheap to generate. But training neural networks on experimental data is a very interesting option, because it allows to include all experimental constraints into the model, which consequently learns to capture phenomena that may be hard to simulate. Here, in order to learn an accurate model for the full experimental measurement setup, we trained a neural network with experimental darkfield color data from several thousand nanostructures. Firstly, we built a forward network, taking as input the nanostructures’ shapes from fabricated samples and predicting the dark-field color for both X and Y polarizations. We then successfully built an inverse tandem network, capable of designing structures with desired color responses. In order to create distinguishable color responses, another deep neural network was trained on the task to map all experimental colors in a regularized color latent space. Sampling equidistant points from this latent space then yields the most distinguishable, yet experimentally feasible colors. The next future step will be to produce samples from the generated structures to test the network’s accuracy. We would like to test how many bits of information we can encode using the darkfield color as readout.

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