By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Abstract Machine learning has transformed many fields and has recently found applications
in chemistry and materials science. The small datasets commonly found in chemistry …

Metal–organic frameworks (MOFs) are materials with a high degree of porosity that can be
used for many applications. However, the chemical space of MOFs is enormous due to the …

In this paper, we review recent advances in the Continuous Fractional Component Monte
Carlo (CFCMC) methodology and present a historic overview of the most important …

Over 14 000 porous, three-dimensional metal–organic framework structures are compiled
and analyzed as a part of an update to the Computation-Ready, Experimental Metal …

Herein, we report a pre‐synthetic pore environment design strategy to achieve stable methyl‐
functionalized metal–organic frameworks (MOFs) for preferential SO2 binding and thus …

Natural hydrogen has been widely detected in many environments. However, up to date, the
knowledge about the occurrence of hydrogen in the geological formation is very limited …

Physisorption of hydrogen in nanoporous materials offers an efficient and competitive
alternative for hydrogen storage. At low temperatures (eg 77 K) and moderate pressures …

Purification of ethylene (C2H4) as the most extensive and output chemical, from complex
multi-components is of great significance but highly challenging. Herein we demonstrate that …

Interpretable machine learning (ML) is applied to accelerate the discovery of promising
metal–organic frameworks (MOFs) for the selective separation of ethane (C 2 H 6) and …