Self-driving laboratories for chemistry and materials science

G Tom, SP Schmid, SG Baird, Y Cao, K Darvish… - Chemical …, 2024 - ACS Publications
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Computational discovery of transition-metal complexes: from high-throughput screening to machine learning

A Nandy, C Duan, MG Taylor, F Liu, AH Steeves… - Chemical …, 2021 - ACS Publications
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 …

Big-data science in porous materials: materials genomics and machine learning

KM Jablonka, D Ongari, SM Moosavi, B Smit - Chemical reviews, 2020 - ACS Publications
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 …

Machine learning for catalysis informatics: recent applications and prospects

T Toyao, Z Maeno, S Takakusagi, T Kamachi… - Acs …, 2019 - ACS Publications
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

Toward autonomous design and synthesis of novel inorganic materials

NJ Szymanski, Y Zeng, H Huo, CJ Bartel, H Kim… - Materials …, 2021 - pubs.rsc.org
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting
opportunity to revolutionize inorganic materials discovery and development. Herein, we …

How to explore chemical space using algorithms and automation

PS Gromski, AB Henson, JM Granda… - Nature Reviews …, 2019 - nature.com
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 …

Extracting knowledge from data through catalysis informatics

AJ Medford, MR Kunz, SM Ewing, T Borders… - Acs …, 2018 - ACS Publications
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 …

[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 …

High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery

K McCullough, T Williams, K Mingle… - Physical Chemistry …, 2020 - pubs.rsc.org
High throughput experimentation in heterogeneous catalysis provides an efficient solution to
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 …