[HTML][HTML] Recent advances in computational modeling of MOFs: From molecular simulations to machine learning
The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation of an
almost boundless number of materials some of which can be a substitute for the traditionally …
almost boundless number of materials some of which can be a substitute for the traditionally …
Big-data science in porous materials: materials genomics and machine learning
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 …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
Electronic structure modeling of metal–organic frameworks
Owing to their molecular building blocks, yet highly crystalline nature, metal–organic
frameworks (MOFs) sit at the interface between molecule and material. Their diverse …
frameworks (MOFs) sit at the interface between molecule and material. Their diverse …
[HTML][HTML] Recent advances, opportunities, and challenges in high-throughput computational screening of MOFs for gas separations
In the last two decades, metal organic frameworks (MOFs) have gained significant attention
as adsorbent and membrane materials for gas separations. Due to the large number and …
as adsorbent and membrane materials for gas separations. Due to the large number and …
Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks
Few hydrogen adsorbents balance high usable volumetric and gravimetric capacities.
Although metal-organic frameworks (MOFs) have recently demonstrated progress in closing …
Although metal-organic frameworks (MOFs) have recently demonstrated progress in closing …
[HTML][HTML] Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design
Materials development has historically been driven by human needs and desires, and this is
likely to continue in the foreseeable future. The global population is expected to reach ten …
likely to continue in the foreseeable future. The global population is expected to reach ten …
Metal–organic frameworks meet scalable and sustainable synthesis
Over the past decade, metal–organic frameworks (MOFs) have emerged as enabling
materials for a wide variety of sustainable technologies, leading to their recent …
materials for a wide variety of sustainable technologies, leading to their recent …
Applications of machine learning in metal-organic frameworks
Abstract Machine learning (ML) is the field of computer science where computing systems
are trained to perform an analysis of provided data to reveal previously unseen trends and …
are trained to perform an analysis of provided data to reveal previously unseen trends and …
ARC–MOF: a diverse database of metal-organic frameworks with DFT-derived partial atomic charges and descriptors for machine learning
Metal–organic frameworks (MOFs) are a class of crystalline materials composed of metal
nodes or clusters connected via semi-rigid organic linkers. Owing to their high-surface area …
nodes or clusters connected via semi-rigid organic linkers. Owing to their high-surface area …
Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors
Abstract Metal-Organic frameworks (MOFs) have been considered for various gas storage
and separation applications. Theoretically, there are an infinite number of MOFs that can be …
and separation applications. Theoretically, there are an infinite number of MOFs that can be …