turboTDDFT 2.0 Hybrid functionals and new algorithms within time-dependent density-functional perturbation theory

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Various forms of real-world data, such as social, financial, and biological networks, can be represented using graphs. An efficient method of analysing this type of data is to extract subgraph patterns, such as cliques, cycles, and motifs, from graphs. For ...

We introduce two new approximation methods for the numerical evaluation of the long-range component of the range-separated Coulomb potential and the approximation of the resulting high dimensional Two-Electron Integrals tensor (TEI) with long-range interac ...

The screening arising from many-body excitations is a crucial quantity for describing absorption and inelastic X-ray scattering (IXS) of materials. Similarly, the electron screening plays a critical role in state-of-the-art approaches for determining the f ...

Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity conditio ...

Understanding the mechanical behavior of silicon nanowiresis essentialfor the implementation of advanced nanoscale devices. Although bendingtests are predominantly used for this purpose, their findings shouldbe properly interpreted through modeling. Variou ...

Recent neutron-diffraction experiments in honeycomb CrI3 quasi-2D ferromagnets have evinced the existence of a gap at the Dirac point in their spin-wave spectra. The existence of this gap has been attributed to strong in-plane Dzyaloshinskii-Moriya or Kita ...

The electron self-interaction is a long-standing problem in density functional theory and is particularly critical in the description of polarons. Polarons are quasiparticles involving charge localization coupled with self-induced lattice distortions. Sinc ...

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

Molecular dynamics (MD) simulations have emerged as a transformative approach to analyse molecular systems at the atomic level, offering valuable insights into complex biological processes. Many biological phenomena can only accurately be described by inco ...

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

We characterize the photochemically relevant conical intersections between the lowest-lying accessible electronic excited states of the different DNA/RNA nucleobases using Cholesky decomposition-based complete active space self-consistent field (CASSCF) al ...

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This k ...

turboTDDFT 2.0 Hybrid functionals and new algorithms within time-dependent density-functional perturbation theory

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Acceleration of graph pattern mining and applications to financial crime

Factorized structure of the long-range two-electron integrals tensor and its application in quantum chemistry

Many-body screening effects in liquid water

koopmans: An Open-Source Package for Accurately and Efficiently Predicting Spectral Properties with Koopmans Functionals

Nanomechanical Modeling of the Bending Response of Silicon Nanowires

Magnon-phonon interactions enhance the gap at the Dirac point in the spin-wave spectra of CrI3 two-dimensional magnets

Self-Interaction and Polarons in Density Functional Theory

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Advancing first principle-based molecular dynamics of biological systems with machine learning

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Characterizing Conical Intersections in DNA/RNA Nucleobases with Multiconfigurational Wave Functions of Varying Active Space Size

De novo design of protein interactions with learned surface fingerprints