Related papers: Incorporating quasiparticle and excitonic properti…
Efficient characterization of surface compositions across high-dimensional materials spaces is critical for accelerating the discovery of surface-dominated functional materials. While X-ray photoelectron spectroscopy allows detailed surface…
Superconducting two-dimensional materials, and in particular few-layer graphene, offer an exciting platform for low-power electronics, yet the origin of their unconventional superconductivity remains an open question. Prevailing theories,…
The quasi-particle band structure and dielectric function for the so-called magic sequence SiGe$_2$Si$_2$Ge$_2$SiGe$_{12}$ (or $\alpha_{12}$) structure [PRL 108, 027401 (2012)] are calculated by many-body perturbation theory (MBPT) within…
The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery…
The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of…
Quasiparticle excitation energies and optical properties of TiO$_{2}$ in the rutile and anatase structures are calculated using many-body perturbation theory methods. Calculations are performed for a frozen crystal lattice; electron-phonon…
The combination of two-dimensional materials into heterostructures offers new opportunities for the design of optoelectronic devices with tunable properties. However, computing electronic and optical properties of such systems using…
Both the intrinsic anisotropic optical materials and fullerene-assembled 2D materials have attracted a lot of interests in fundamental science and potential applications. The synthesis of a monolayer (ML) fullerene makes the combination of…
Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. In this study, we investigate and evaluate the applicability of QML models for binary classification of genome sequence data by…
Gaussian boson sampling (GBS) has emerged as a promising quantum computing paradigm, demonstrating its potential in various applications. However, most existing works focus on theoretical aspects or simple tasks, with limited exploration of…
This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 1011 solid-state compounds. The ML model uses a set of 25…
We use state-of-the art GW-BSE formalism to study electronic structure and optical properties of oxygen vacancies in $\alpha$-alumina. Many body perturbation theory within GW approximation in recent years have been used extensively to study…
We report a comprehensive first-principles study of the electronic and optical properties of recently identified exfoliable one-dimensional semiconducting materials, focusing on chalcogenide-based atomic chains derived from van der…
Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify…
Quasi-periodic eruptions (QPEs) are high-amplitude, soft X-ray bursts recurring every few hours, associated with supermassive black holes. Many interpretations for QPEs were proposed since their recent discovery in 2019, including extreme…
In this work we present a new procedure to compute optical spectra including excitonic effects and approximated quasiparticle corrections with reduced computational effort. The excitonic effects on optical spectra are included by solving…
A cardinal obstacle to performing quantum-mechanical simulations of strongly-correlated matter is that, with the theoretical tools presently available, sufficiently-accurate computations are often too expensive to be ever feasible. Here we…
We performed first-principles density functional theory (DFT) and GW calculations to investigate electronic structures of bulk and few-layer PdSe2. We obtained the quasiparticle band structure of bulk PdSe2, and the obtained energy gap…
Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be…
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…