We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Nowadays, many chemical investigations are supported by routine calculations of molecular
structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share …

The powerful independent gradient model (IGM) method has been increasingly popular in
visual analysis of intramolecular and intermolecular interactions in recent years. However …

Covalent organic frameworks (COFs) represent an emerging class of organic photocatalysts.
However, their complicated structures lead to indeterminacy about photocatalytic active sites …

This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

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 review covers a family of atomistic, mostly quantum chemistry (QC) based
semiempirical methods for the fast and reasonably accurate description of large molecules …

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 …

Metal-metal bonding interactions can engender outstanding magnetic properties in bulk
materials and molecules, and examples abound for the transition metals. Extending this …

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