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 Machine learning (ML) has proven to be an effective tool for accelerating the
discovery of high-performance polymeric membranes and materials for gas separation …

In order to study the mechanism of interaction between stress, pore-fissure system, and
methane adsorption. Adopting a novel combination of experimental and simulation methods …

We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications
and identify the minimal set of crystallographic descriptors underpinning the most important …

High-throughput calculations based on molecular simulations to predict the adsorption of
molecules inside metal–organic frameworks (MOFs) have become a useful complement to …

A collection of more than 20,000 experimentally derived crystal structures for metal–organic
frameworks (MOFs) that do not have two-or three-dimensional covalently bonded networks …

Highlights•Atomistic modeling techniques for microporous feature analysis.•In silico
predictions of adsorption with flexible frameworks.•Data-driven methods are positioned to …

The vast majority of molecular simulations of adsorption in porous materials make use of the
assumption that the framework of the porous material may be held rigid without significantly …

Investigation of mixed-gas sorption is necessary for robust design and optimization of
membrane-based processes. While sorption models for glassy polymers are well …

Molecular diffusion in nanoporous materials is important in determining the rate of
equilibration of various adsorption processes and plays a pivotal role in kinetic separations …