MaX paper

The authors introduce HP, an implementation of density-functional perturbation theory, designed to compute Hubbard parameters (on-site U and inter-site V) in the framework of DFT+U and DFT+U+V. The code does not require the use of computationally expensive supercells of the traditional linear-response approach; instead, unit cells are used with monochromatic perturbations that significantly reduce the computational cost of determining Hubbard parameters. HP is an open-source software distributed under the terms of the GPL as a component of Quantum ESPRESSO.

The authors introduce turboMagnon, an implementation of the Liouville-Lanczos approach to linearized time-dependent density-functional theory, designed to simulate spin-wave spectra in solid-state materials. The code is based on the noncollinear spin-polarized framework and the self-consistent inclusion of spin-orbit coupling that allow to model complex magnetic excitations. The spin susceptibility matrix is computed using the Lanczos recursion algorithm that is implemented in two flavors - the non-Hermitian and the pseudo-Hermitian one.

The authors investigate how parameters of the model of semi-infinite graphene based on a graphene nanoribbon under periodic boundary conditions affect the accuracy of ab initio calculations of reactions at graphene edges by the example of the first stage of reconstruction of zigzag graphene edges, formation of a pentagon-heptagon pair. It is shown that to converge properly the results, the nanoribbon should consist of at least 6 zigzag rows and periodic images of the pair along the nanoribbon axis should be separated by at least 6 hexagons.

Fermi level pinning (FLP) in metal-MoS₂ contacts induces large Schottky barrier heights which in turn results in large contact resistances. In this work, we made use of Density Functional Theory (DFT) to study the origin of FLP in MoS₂ contacts with a variety of metals. We also reported how the Fermi level de-pinning could be attained by controlling the distance between the metal and MoS₂. In this respect, the metal-MoS₂ contacts can be engineered by means of the insertion of proper buffer layers and the use of back-gated structures.

In this paper, the authors report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT).

Engineering oxygen octahedra rotation patterns in ABO3 perovskites is a powerful route to design functional materials. Here we propose a strategy that exploits point defects that create local electric dipoles and couple to the oxygen sublattice, enabling direct actuation on the rotational degrees of freedom. This approach, which relies on substituting an A site with a smaller ion, paves a way to couple dynamically octahedra rotations to external electric fields.

The authors perform a density functional theory study of the effects of oxygen adsorption on the structural and electronic properties of Gr/Co(0001) and Gr/Co/Ir(111) interfaces. In both interfaces, the graphene-Co distance increases with increasing O concentration. The oxygen intercalation effectively decreases the electronic interaction, preventing the hybridization of graphene states with Co d orbitals, hence (partly) restoring the typical Dirac cone of pristine graphene.

In this paper, the authors investigate the performance of beyond-GW approaches in many-body perturbation theory by addressing atoms described within the spherical approximation via a dedicated numerical treatment based on B-splines and spherical harmonics. The authors consider the GW, second Born (2B), and GW + second order screened exchange (GW+SOSEX) self-energies and use them to obtain ionization potentials from the quasi-particle equation (QPE) solved perturbatively on top of independent-particle calculations.

In this article appearing on Physical Reviews B, an international team comprised of young researchers from Italy and Switzerland show how, contrary to popular assumptions, predictions from machine learning potentials almost exclusively occur in an extrapolation regime. 

In recent years, modeling and simulation of materials have become indispensable to complement experiments in materials design. High-throughput simulations increasingly aid researchers in selecting the most promising materials for experimental studies or by providing insights inaccessible by experiment. However, this often requires multiple simulation tools to meet the modeling goal.