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 …
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 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 …
Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning
In this work, the algal biomass productivity and its lipid content were explored using a
database containing 4670 instances extracted from the experimental results reported in 102 …
database containing 4670 instances extracted from the experimental results reported in 102 …
Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
The use of machine learning (ML) in catalysis has been significantly increased in recent
years due to the astonishing developments in data processing technologies and the …
years due to the astonishing developments in data processing technologies and the …
Development of the design and synthesis of metal–organic frameworks (MOFs)–from large scale attempts, functional oriented modifications, to artificial intelligence (AI …
Owing to the exceptional porous properties of metal–organic frameworks (MOFs), there has
recently been a surge of interest, evidenced by a plethora of research into their design …
recently been a surge of interest, evidenced by a plethora of research into their design …
Machine learning-driven insights into defects of zirconium metal–organic frameworks for enhanced ethane–ethylene separation
Y Wu, H Duan, H ** - Chemistry of Materials, 2020 - ACS Publications
Structural defects in metal–organic frameworks (MOFs) have the potential to yield desirable
properties that could not be achieved by “defect-free” crystals, but previous works in this …
properties that could not be achieved by “defect-free” crystals, but previous works in this …
Design, parameterization, and implementation of atomic force fields for adsorption in nanoporous materials
Molecular simulations are an excellent tool to study adsorption and diffusion in nanoporous
materials. Examples of nanoporous materials are zeolites, carbon nanotubes, clays, metal …
materials. Examples of nanoporous materials are zeolites, carbon nanotubes, clays, metal …
Machine learning and descriptor selection for the computational discovery of metal-organic frameworks
ABSTRACT Metal-organic frameworks (MOFs), crystalline materials with high internal
surface area and pore volume, have demonstrated great potential for many applications. In …
surface area and pore volume, have demonstrated great potential for many applications. In …
Accelerating the selection of covalent organic frameworks with automated machine learning
Covalent organic frameworks (COFs) have the advantages of high thermal stability and
large specific surface and have great application prospects in the fields of gas storage and …
large specific surface and have great application prospects in the fields of gas storage and …