The atomic simulation environment (ASE) is a software package written in the Python
programming language with the aim of setting up, steering, and analyzing atomistic …

Machine learning encompasses tools and algorithms that are now becoming popular in
almost all scientific and technological fields. This is true for molecular dynamics as well …

Abstract Development of efficient catalysts for the direct hydrogenation of CO2 to methanol is
essential for the valorization of this abundant feedstock. Here we show that a silica …

The climbing image nudged elastic band method (CI-NEB) is used to identify reaction
coordinates and to find saddle points representing transition states of reactions. It can make …

G protein-coupled receptors (GPCRs) are the most common proteins targeted by approved
drugs. A complete mechanistic elucidation of large-scale conformational transitions …

Computational material discovery is under intense study owing to its ability to explore the
vast space of chemical systems. Neural network potentials (NNPs) have been shown to be …

To further improve the intrinsic reactivity of single-atom catalysts (SACs), the controllable
modification of a single site by coordinating with a second neighboring metal atom …

The loss functions of deep neural networks are complex and their geometric properties are
not well understood. We show that the optima of these complex loss functions are in fact …

Solid-state batteries are on the roadmap for commercialization as the next generation of
batteries because of their potential for improved safety, power density, and energy density …

The local coordination structure of metal sites essentially determines the performance of
supported metal catalysts. Using a surface defect enrichment strategy, we successfully …