Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
[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 …
[HTML][HTML] Understanding the diversity of the metal-organic framework ecosystem
Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal
nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over …
nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over …
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 …
Material evolution with nanotechnology, nanoarchitectonics, and materials informatics: what will be the next paradigm shift in nanoporous materials?
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 …
society. With the shift toward sustainable living, it is anticipated that the development of …
Machine learning meets with metal organic frameworks for gas storage and separation
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
Machine learning: accelerating materials development for energy storage and conversion
With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …
increasingly important on a global scale. Therefore, the exploration of novel materials for …
CO2 Capture and Separations Using MOFs: Computational and Experimental Studies
This Review focuses on research oriented toward elucidation of the various aspects that
determine adsorption of CO2 in metal–organic frameworks and its separation from gas …
determine adsorption of CO2 in metal–organic frameworks and its separation from gas …
Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks
Although the tailored metal active sites and porous architectures of MOFs hold great promise
for engineering challenges ranging from gas separations to catalysis, a lack of …
for engineering challenges ranging from gas separations to catalysis, a lack of …
Trends in Solid Adsorbent Materials Development for CO2 Capture
A recent report from the United Nations has warned about the excessive CO2 emissions and
the necessity of making efforts to keep the increase in global temperature below 2° C …
the necessity of making efforts to keep the increase in global temperature below 2° C …