Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
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 …
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 for catalysis informatics: recent applications and prospects
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …
components to maintaining an ecological balance in the future. Recent revolutions made in …
Toward autonomous design and synthesis of novel inorganic materials
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting
opportunity to revolutionize inorganic materials discovery and development. Herein, we …
opportunity to revolutionize inorganic materials discovery and development. Herein, we …
How to explore chemical space using algorithms and automation
Although extending the reactivity of a given class of molecules is relatively straightforward,
the discovery of genuinely new reactivity and the molecules that result is a wholly more …
the discovery of genuinely new reactivity and the molecules that result is a wholly more …
Extracting knowledge from data through catalysis informatics
Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and
materials informatics but with distinctive challenges arising from the dynamic, surface …
materials informatics but with distinctive challenges arising from the dynamic, surface …
[BOOK][B] Ammonia synthesis catalysts: innovation and practice
H Liu - 2013 - books.google.com
This book provides a review of worldwide developments in ammonia synthesis catalysts
over the last 30 years. It focuses on the new generation of Fe1-xO based catalysts and …
over the last 30 years. It focuses on the new generation of Fe1-xO based catalysts and …
High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery
High throughput experimentation in heterogeneous catalysis provides an efficient solution to
the generation of large datasets under reproducible conditions. Knowledge extraction from …
the generation of large datasets under reproducible conditions. Knowledge extraction from …
Machine learning applied to zeolite synthesis: the missing link for realizing high-throughput discovery
M Moliner, Y Román-Leshkov… - Accounts of chemical …, 2019 - ACS Publications
Conspectus Zeolites are microporous crystalline materials with well-defined cavities and
pores, which can be prepared under different pore topologies and chemical compositions …
pores, which can be prepared under different pore topologies and chemical compositions …