Renowned for their high porosity and structural diversity, metal–organic frameworks (MOFs)
are a promising class of materials for a wide range of applications. In recent decades, with …

Abstract Metal–organic frameworks (MOFs), renowned for their exceptional porosity and
crystalline structure, stand at the forefront of gas adsorption and separation applications …

This study reviews copper-based metal–organic frameworks (Cu-MOFs) designed for
enhanced gas adsorption and separation. These MOFs exhibit strategically designed …

We present enhancements in Monte Carlo simulation speed and functionality within an open-
source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant …

Nanomaterials have revolutionized the battery industry by enhancing energy storage
capacities and charging speeds, and their application in hydrogen (H2) storage likewise …

Adsorbent-based hydrogen storage systems offer a potential solution to current challenges
in hydrogen storage, particularly those requiring high pressures or cryogenic temperatures …

This Perspective explores the integration of machine learning potentials (MLPs) in the
research of heterogeneous catalysis, focusing on their role in identifying in situ active sites …

The energy demand for traditional vapor-compressed technology for space cooling
continues to soar year after year due to global warming and the increasing human …

The machine learning potential has emerged as a promising approach for addressing the
accuracy-versus-efficiency dilemma in molecular modeling. Efficiently exploring chemical …

The advent of machine learning potentials (MLPs) provides a unique opportunity to access
simulation time scales and to directly compute physicochemical properties that are typically …