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According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods…
The RPA long range correlations are known to play a significant role in understanding the depletion of single particle-hole states observed in (e, e') and (e, e'p) measurements. Here the Random Phase Approximation (RPA) theory, implemented…
Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning…
We present a detailed study of the coupling-constant-averaged exchange-correlation hole density at a jellium surface, which we obtain in the random-phase approximation (RPA) of many-body theory. We report contour plots of the exchange-only…
Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in…
The time-dependent exchange-correlation potential has an unusual task in directing fictitious non-interacting electrons to move with exactly the same probability density as true interacting electrons. This has intriguing implications for…
Electronic correlations in the ground state of an idealized infinite-layer high-Tc compound are computed using the ab-initio method of local ansatz. Comparisons are made with the local-density approximation (LDA) results, and the…
The important role of the electron-phonon interaction (EPI) in explaining the properties of the normal state and pairing mechanism in high-T$_{c}$ superconductors (HTSC) is discussed. A number of experimental results are analyzed such as:…
The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture, and is central to modern electronic structure theory. It also underpins the computation…
Community detection plays a crucial role in understanding the structural organization of complex networks. Previous methods, particularly those from statistical physics, primarily focus on the analysis of mesoscopic network structures and…
Prediction intervals offer an effective tool for quantifying the uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to…
We perform a numerical simulation of mapping of charge confined in quantum dots by the scanning probe technique. We solve the few-electron Schr\"odinger equation with the exact diagonalization approach and evaluate the energy maps in…
The theorems of density functional theory (DFT) and reduced density matrix functional theory (RDMFT) establish a bijective map between the external potential of a many-body system and its electron density or one-particle reduced density…
We derive a local approximation for the correlation energy in two-dimensional electronic systems. In the derivation we follow the scheme originally developed by Colle and Salvetti for three dimensions, and consider a Gaussian approximation…
The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effective Hamiltonian requires sufficiently large data sets, which…
We propose a novel approach to electron correlation for multireference systems. It is based on particle-hole (ph) and particle-particle (pp) theories in the second-order, developed in the random phase approximation (RPA) framework for…
Coupled cluster theory with a Kohn-Sham reference (KS-CC) can dramatically outperform its Hartree-Fock counterpart for strongly correlated systems, but the origin of these improvements has remained unclear. Here we demonstrate that these…
Predicting protein interactions is one of the more interesting challenges of the post-genomic era. Many algorithms address this problem as a binary classification problem: given two proteins represented as two vectors of features, predict…
We have performed high resolution photoemission study of substitutionally disordered alloys Cu-Pt, Cu-Pd, Cu-Ni, and Pd-Pt. The ratios between alloy spectra and pure metal spectra are found to have dips at the Fermi level when the residual…
We introduce a novel energy functional for ground-state electronic-structure calculations. Its fundamental variables are the natural spin-orbitals of the implied singlet many-body wave function and their joint occupation probabilities. The…