2023/01/05 by Calin-Andrei Pantis-Simut, Amanda Teodora Preda, Pantis-Simut, Calin-Andrei +7 · 2 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Advanced Electron Microscopy Techniques and Applications #FOS: Physical sciences #Image Processing Techniques and Applications #Machine Learning in Materials Science #Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
paper · pdf · doi:10.48550/arxiv.2301.02122
openalex publication_date 2023/01/05 · openalex created_date 2023/01/08 · openalex updated_date 2026/07/28
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 \tt 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.