Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

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 …

Owing to their molecular building blocks, yet highly crystalline nature, metal–organic
frameworks (MOFs) sit at the interface between molecule and material. Their diverse …

Materials science and chemistry have played a central and significant role in advancing
society. With the shift toward sustainable living, it is anticipated that the development of …

The existence of a very large number of porous materials is a great opportunity to develop
innovative technologies for carbon dioxide (CO2) capture to address the climate change …

This research focused on the efficient collection of experimental metal–organic framework
(MOF) data from scientific literature to address the challenges of accessing hard-to-find data …

Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that
have great potential to revolutionize applications such as gas storage, molecular …

The scientific and technological progresses of the chemical and materials science
disciplines lead to a significant amount of numerical and textual data stored in the published …

This research was focused on the efficient collection of experimental Metal-Organic
Framework (MOF) data from scientific literature to address the challenges of accessing hard …

Abstract Metal-organic frameworks (MOFs), as typical porous materials, have been widely
used for gas storage. However, impurities usually coexist in the stored gas, which will affect …