Related papers: Learning on-top: regressing the on-top pair densit…

We study self-consistency for the pulsar polar cap model. In general, there will remain an unscreened electric field at the pair production front (PPF). We have derived the condition of electric field screening by pairs in the presence of…

Plane-wave electronic-structure predictions based upon orbital-dependent density-functional theory (OD-DFT) approximations, such as hybrid density-functional methods and self-interaction density-functional corrections, are severely affected…

Efficiently recovering dynamic correlation in strongly correlated systems without incurring prohibitive computational costs remains a central challenge in quantum chemistry. In this Perspective, we review and benchmark methods capable of…

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. We…

Multireference electron correlation methods describe static and dynamical electron correlation in a balanced way, and therefore, can yield accurate and predictive results even when single-reference methods or multiconfigurational…

Electron density $\rho(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes in $\rho(\vec{r})$ distributions are often used to…

Density functional theory (DFT) is one of the main methods in Quantum Chemistry that offers an attractive trade off between the cost and accuracy of quantum chemical computations. The electron density plays a key role in DFT. In this work,…

The alignment of the frontier orbital energies of an adsorbed molecule with the substrate Fermi level at metal-organic interfaces is a fundamental observable of significant practical importance in nanoscience and beyond. Typical density…

With the advent of very large volume, wide-angle photometric redshift surveys like e.g. Pan-STARRS, DES, or PAU, which aim at using the spatial distribution of galaxies as a means to constrain the equation of state parameter of dark energy,…

We present a machine-learning workflow for the calculation of the infrared spectrum of molecules, and more generally of other temperature-dependent electronic observables. The main idea is to use the Jacobi-Legendre cluster expansion to…

The study of matter under extreme densities and temperatures as they occur e.g. in astrophysical objects and nuclear fusion applications has emerged as one of the most active frontiers in physics, material science, and related disciplines.…

We devise a scheme to characterize tunneling of an excess electron shared by a pair of tunnel-coupled dangling bonds on a silicon surface -- effectively a two-level system. Theoretical estimates show that the tunneling should be highly…

The "density-density" correlation function of conduction electrons in metal is investigated. It is shown, that the asymptotic behaviour of the CF depends on the shape and the local geometry of the Fermi surface. In particular, the exponent…

We describe a novel approach to statistical learning from particles tracked while moving in a random environment. The problem consists in inferring properties of the environment from recorded snapshots. We consider here the case of a fluid…

The accurate resolution of the chemical properties of strongly correlated systems, such as biradicals, requires the use of electronic structure theories that account for both multi-reference as well as dynamic correlation effects. A variety…

One key task in virtual screening is to accurately predict the binding affinity ($\triangle$$G$) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the…

Band structure of metallic sodium cobaltate Na$_x$CoO$_2$ ($x$=0.33, 0.48, 0.61 0.72) has been investigated by local density approximation+Hubbard $U$ (LDA+$U$) method and within Gutzwiller approximation for the Co-$t_{2g}$ manifold.…

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly…

We investigate the impact of dissipation on the energy balance in the electron dynamics of metal clusters excited by strong electro-magnetic pulses. The dynamics is described theoretically by Time-Dependent Density-Functional Theory (TDDFT)…

Machine learning models for 3D molecular property prediction typically rely on atom-based representations, which may overlook subtle physical information. Electron density maps -- the direct output of X-ray crystallography and cryo-electron…