In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

This article summarizes technical advances contained in the fifth major release of the Q-
Chem quantum chemistry program package, covering developments since 2015. A …

Analysis of electron wavefunction is a key component of quantum chemistry investigations
and is indispensable for the practical research of many chemical problems. After more than …

The WIEN2k program is based on the augmented plane wave plus local orbitals (APW+ lo)
method to solve the Kohn–Sham equations of density functional theory. The APW+ lo …

Hydrogen energy-based electrochemical energy conversion technologies offer the promise
of enabling a transition of the global energy landscape from fossil fuels to renewable energy …

The so-called D4 model is presented for the accurate computation of London dispersion
interactions in density functional theory approximations (DFT-D4) and generally for atomistic …

Machine learning (ML) is transforming all areas of science. The complex and time-
consuming calculations in molecular simulations are particularly suitable for an ML …

Q uantum ESPRESSO is an integrated suite of open-source computer codes for quantum
simulations of materials using state-of-the-art electronic-structure techniques, based on …

Among the many challenges in medicine, the treatment and cure of cancer remains an
outstanding goal given the complexity and diversity of the disease. Nanotheranostics, the …

Abstract Machine-learned force fields combine the accuracy of ab initio methods with the
efficiency of conventional force fields. However, current machine-learned force fields …