Accurate and efficient tools for calculating the ground state properties of interacting quantum systems are essential in the design of nanoelectronic devices. The exact diagonalization method fully accounts for the Coulomb interaction beyond mean field approximations and it is regarded as the gold-standard for few electron systems. However, by increasing the number of instances to be solved, the computational costs become prohibitive and new approaches based on machine learning techniques can provide a significant reduction in computational time and resources, maintaining a reasonable accuracy. Here, we employ pix2pix, a general-purpose image-to-image translation method based on conditional generative adversarial network (cGAN), for predicting ground state densities from randomly generated confinement potentials. Other mappings were also investigated, like potentials to non-interacting densities and the translation from non-interacting to interacting densities. The architecture of the cGAN was optimized with respect to the internal parameters of the generator and discriminator. Moreover, the inverse problem of finding the confinement potential given the interacting density can also be approached by the pix2pix mapping, which is an important step in finding near-optimal solutions for confinement potentials.

Phosphorene is a graphene-like material with an intermediate band gap, in contrast to zero-gap graphene and large-gap dichalcogenides or hexagonal boron nitride (hBN), which makes it more suitable for nanoelectronic devices. However, inducing band-gap modulation in freestanding phosphorene nanoribbons (PNRs) is problematic, as high in-plane electric fields are necessary to close the gap. We perform here a detailed investigation concerning the substrate influence on the electric-field control exerted by an external gate, using the density functional theory–non-equilibrium Green’s functions (DFT-NEGF) framework. It is established that the interaction with a hexagonal boron nitride supporting layer significantly enhances the gap modulation. Furthermore, we address the issue of contacting the PNRs, by using conducting graphene nanoribbons embedded in the support hBN layer. Within this setup, a measurable spin polarization is achieved owing to the anti-ferromagnetic coupling between the edges of the graphene nanoribbons.

Ab initio molecular dynamics simulations of Ca _n @C ₆₀ ( n = 1, 2, 3, 4) endohedral fullerenes indicate a correlated movement of the Ca atoms. The dynamical properties are recovered using a mapped force field by artificial neural networks.

Interconnecting nanodevices with the aim of assembling quantum computing architectures is one of the current outstanding challenges. At the nanoscale, the quantum interconnects become comparable in complexity with the active devices and should be treated on equal footing. In addition, they can play an active role in the switching properties. Here, we investigate the charge localization in neuromorphic bi-dimensional systems, which serve as quantum interconnects (QIs) between quantum dot registers. We define a device structure where, by manipulating the charging of a floating gate array, one defines the QI potential map, which can host a few interacting electrons. The ground state charge density may be extracted by measuring the tunneling current perpendicular to the device surface, yielding a convoluted image of the electron distribution. Using image-to-image translation methods, we achieve the mapping of the charge density from the confinement potential, as well as by deconvoluting the tunneling current map, which can be obtained by a direct measurement. Thus, we provide a proof-of-concept for a reconfigurable device, which can be used to design quantum many-electron devices.

Density functional theory (DFT) is the workhorse in computational materials science and quantum chemistry, widely used for systems ranging from molecules to surfaces and bulk systems. The electronic properties are determined from the solution of the Kohn-Sham equations, which is obtained iteratively, starting from a best-guess initial charge density. If the number of atoms in the simulation box increases to a few hundred atoms and a large class of compounds is required to be screened, the computational effort becomes significant even when linear-scaling DFT codes are employed. We introduce the mcv2v-cGAN model based on conditional generative-adversarial networks (cGANs), which performs a multi-channel voxel-to-voxel mapping from readily available structural information to the final self-consistent charge density. In order to preserve the DFT-level accuracy, the predicted density is used as a better initial guess density, which renders a substantial decrease in the number of steps in the self-consistent loop. The mcv2v-cGAN model succeeds in making accurate predictions for the self-consistent charge densities and derived observables, using as test cases extended sets of organic molecules and defective silicon supercells. Moreover, a surrogate model for molecular dynamics using the predicted charge densities produced consistent trajectories while significantly reducing the computational effort.