Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

This review spotlights the role of atomic‐level modeling in research on metal‐organic
frameworks (MOFs), especially the key methodologies of density functional theory (DFT) …

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made
significant progress and provided benefits in the fields of chemistry and material science …

A major obstacle for machine learning (ML) in chemical science is the lack of physically
informed feature representations that provide both accurate prediction and easy …

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

Direct air capture (DAC) of CO2 with porous adsorbents such as metal− organic frameworks
(MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for …

With increasing pace, crystalline open frameworks are moving to larger scale, mature
applications that stretch as broadly as catalysis, separation, water purification, adsorption …

Owing to the exceptional porous properties of metal–organic frameworks (MOFs), there has
recently been a surge of interest, evidenced by a plethora of research into their design …

Hydrogen (H 2) is a promising energy carrier for achieving net zero carbon emissions. Metal
organic frameworks (MOFs) and covalent organic frameworks (COFs) have emerged as …

The development of new functional materials is crucial for addressing global challenges
such as clean energy or the discovery of new drugs and antibiotics. Functional material …