Abstract
Abstract
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.
Funder
Romanian Ministry of Research, Innovation and Digitalization, CNCS - UEFISCDI
Subject
Artificial Intelligence,Human-Computer Interaction,Software
Reference55 articles.
1. Machine learning-based approach: global trends, research directions and regulatory standpoints;Pugliese;Data Sci. Manage.,2021
2. Review of deep learning: concepts, cnn architectures, challenges, applications, future directions;Alzubaidi;J. Big Data,2021
3. Machine learning: algorithms, real-world applications and research directions;Sarker;SN Comput. Sci.,2021
4. Recent advances and applications of machine learning in solid-state materials science;Schmidt;npj Comput. Mater.,2019
5. Learning matter: materials design with machine learning and atomistic simulations;Axelrod;Acc. Mater. Res.,2022
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