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 …
Combining machine learning and molecular simulations to unlock gas separation potentials of MOF membranes and MOF/polymer MMMs
Due to the enormous increase in the number of metal-organic frameworks (MOFs),
combining molecular simulations with machine learning (ML) would be a very useful …
combining molecular simulations with machine learning (ML) would be a very useful …
Bayesian optimization of nanoporous materials
Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …
Machine learning accelerates the investigation of targeted MOFs: performance prediction, rational design and intelligent synthesis
Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …
used in various emerging fields due to their large specific surface area, high porosity and …
Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation
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 …
metal–organic frameworks (MOFs) for the selective separation of ethane (C 2 H 6) and …
Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
Metal–organic frameworks (MOFs) have attracted tremendous interest because of their
tunable structures, functionalities, and physiochemical properties. The nearly infinite …
tunable structures, functionalities, and physiochemical properties. The nearly infinite …
Module-based machine learning models using sigma profiles of organic linkers to predict gaseous adsorption in metal-organic frameworks
Abstract Background Metal-organic frameworks (MOFs) have drawn considerable attention
for their potential in adsorption applications, such as gas separation and storage. Machine …
for their potential in adsorption applications, such as gas separation and storage. Machine …
Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling
A novel integrated machine learning (ML) framework, consisting of structure decomposition,
feature integration and predictive modeling, is proposed to correlate MOF structures with gas …
feature integration and predictive modeling, is proposed to correlate MOF structures with gas …
Research progress on the adsorption of sulfocompounds in flue gas
S Guo, Q Yu, S Zhao, X Tang, Y Wang, Y Ma… - Chemical Engineering …, 2023 - Elsevier
Sulfocompounds in flue gas emitted by various industries, not only affect the stability of
pipelines and subsequent devices but also cause air pollution when discharged into the …
pipelines and subsequent devices but also cause air pollution when discharged into the …