Highly accurate potential energy surfaces are critically important for chemical reaction
dynamics. The large number of degrees of freedom and the intricate symmetry adaption …

In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

Machine learning (ML) is increasingly becoming a common tool in computational chemistry.
At the same time, the rapid development of ML methods requires a flexible software …

Abstract Machine learned interatomic interaction potentials have enabled efficient and
accurate molecular simulations of closed systems. However, external fields, which can …

The activation of alkanes on metal catalysts may involve a precursor-mediated mechanism,
in which impinging molecules are first trapped on the catalyst surface to form an adsorbed …

A transition from a linear to a circular economy is the only alternative to reduce current
pressures in natural resources. Our society must redefine our material sources, rethink our …

Equilibration dynamics of hot oxygen atoms following the dissociation of O2 on Pd (100) and
Pd (111) surfaces are investigated by molecular dynamics simulations based on a scalable …

In this work, we investigate the decomposition dynamics of formate (HCO2), which is an
important reaction intermediate in many catalytic processes, on three model catalyst …

Atomistic neural network potentials have achieved great success in accelerating atomistic
simulations in complicated systems in recent years. They are typically based on the atomic …

The reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and
hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies …