A number of 2D layered perovskites A2PbI4 and BPbI4, with A and B mono- and divalent ammonium and imidazolium cations, have been modeled with different theoretical methods. The periodic structures have been optimized (both in monoclinic and in triclinic systems, corresponding to eclipsed and staggered arrangements of the inorganic layers) at the DFT level, with hybrid functionals, Gaussian-type orbitals and dispersion energy corrections. With the same methods, the various contributions to the solid stabilization energy have been discussed, separating electrostatic and dispersion energies, organic-organic intralayer interactions and H-bonding effects, when applicable. Then the electronic band gaps have been computed with plane waves, at the DFT level with scalar and full relativistic potentials, and including the correlation energy through the GW approximation. Spin orbit coupling and GW effects have been combined in an additive scheme, validated by comparing the computed gap with well known experimental and theoretical results for a model system. Finally, various contributions to the computed band gaps have been discussed on some of the studied systems, by varying some geometrical parameters and by substituting one cation in another's place.

The chemical structure of a molecule influences the molecular conformation in the solid-state affecting the crystal packing, which in turn drives the charge carrier transport anisotropy.

In this paper, we showcase the innovative concept of implementing Oscillatory Neural Networks (ONNs) for neuromorphic computing with beyond-CMOS devices based on vanadium dioxide to mimic neurons and resistors to emulate synapses. We explore ONN technology potentials from device to analog circuit-level simulations. We report that ONN behaves like an associative memory and can implement energy-based models such as Hopfield Neural Networks on edge devices. Finally, as a proof of concept, a reconfigurable digital ONN is implemented on FPGA for pattern recognition tasks.

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