Related papers: Machine learning for impurity charge-state transit…
In the current Industrial 4.0 revolution, Prognostics and Health Management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the…
In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often…
We study a pseudogap Anderson-Holstein model of a magnetic impurity level that hybridizes with a conduction band whose density of states vanishes in power-law fashion at the Fermi energy, and couples, via its charge, to a nondispersive…
Spontaneous symmetry breaking mechanism in quantum phase transitions manifests the existence of degenerate groundstates in broken symmetry phases. To detect such degenerate groundstates, we introduce a quantum fidelity as an overlap…
Charge traps in the semiconductor bulk (bulk charge traps) make it difficult to predict the electric field within wide-bandgap semiconductors. The issue is the daunting number of bulk charge-trap candidates which means the treatment of bulk…
The synthesis of transition metal heterostructures is currently one of the most vivid fields in the design of novel functional materials. In this paper we propose a simple scheme to predict \emph{band alignment }and \emph{charge transfer}…
High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe…
The reliability of atomistic simulations depends on the quality of the underlying energy models providing the source of physical information, for instance for the calculation of migration barriers in atomistic Kinetic Monte Carlo…
The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a…
Gaussian Process Regression-based Gaussian Approximation Potential has been used to develop machine-learned interatomic potentials having density-functional accuracy for free sodium clusters. The training data was generated from a large…
Machine learning force fields (MLFFs), which employ neural networks to map atomic structures to system energies, effectively combine the high accuracy of first-principles calculation with the computational efficiency of empirical force…
We use density functional theory (DFT) with non-empirically tuned screened range-separated hybrid (SRSH) functionals to calculate the electronic properties of native zinc and oxygen vacancy point defects in ZnO, and we predict their defect…
A generalized version of the fidelity susceptibility of single-band and multi-orbital Hubbard models is systematically studied using single-site dynamical mean-field theory in combination with a hybridization expansion continuous-time…
By considering transition-metal (Shiba-Rusinov model) and rare-earth metal impurities (Abrikosov-Gor'kov theory) effect on a many-body system, i.e., a BCS s-wave superconductor, quantum bipartite entanglement of two electrons of the Cooper…
We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML…
Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including adaptive photodetectors and reconfigurable photovoltaic…
Heterogeneous interfaces are central to many energy-related applications in the nanoscale. From the first-principles electronic structure perspective, one of the outstanding problems is accurately and efficiently calculating how the…
Lattice thermal conductivity (TC) of semiconductors is crucial for various applications, ranging from microelectronics to thermoelectrics. Data-driven approach can potentially establish the critical composition-property relationship needed…
We present electronic structure and transport calculations for hydrogen and lithium chains, using density functional theory and scattering theory on the Green's function level, to systematically study impurity effects on the transmission…
Transition-state searches are central to understanding reaction mechanisms, but the high computational cost of density-functional theory (DFT) limits their application in high-throughput catalyst and materials discovery. Machine-learned…