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] Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS)–a state-of-the-art review
Carbon capture, utilisation and storage (CCUS) will play a critical role in future
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
The role of machine learning in the understanding and design of materials
Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …
enable us to systematically find novel materials, which can have huge technological and …
[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 …
[HTML][HTML] Sorption-enhanced steam methane reforming for combined CO2 capture and hydrogen production: a state-of-the-art review
Abstract The European Commission have just stated that hydrogen would play a major role
in the economic recovery of post-COVID-19 EU countries. Hydrogen is recognised as one of …
in the economic recovery of post-COVID-19 EU countries. Hydrogen is recognised as one of …
[HTML][HTML] Machine learning in materials informatics: recent applications and prospects
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …
developments and the resounding successes of data-driven efforts in other domains …
[HTML][HTML] Materials discovery and design using machine learning
Y Liu, T Zhao, W Ju, S Shi - Journal of Materiomics, 2017 - Elsevier
The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …
Emerging Trends in Sustainable CO2‐Management Materials
With the rising level of atmospheric CO2 worsening climate change, a promising global
movement toward carbon neutrality is forming. Sustainable CO2 management based on …
movement toward carbon neutrality is forming. Sustainable CO2 management based on …
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 …
Inverse design of nanoporous crystalline reticular materials with deep generative models
Reticular frameworks are crystalline porous materials that form via the self-assembly of
molecular building blocks in different topologies, with many having desirable properties for …
molecular building blocks in different topologies, with many having desirable properties for …