By how much can closed-loop frameworks accelerate computational materials discovery?-Reference-Cited by-同舟云学术

By how much can closed-loop frameworks accelerate computational materials discovery?

Published:2023 Issue:4 Volume:2 Page:1112-1125
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Kavalsky Lance¹^ORCID,Hegde Vinay I.²,Muckley Eric²,Johnson Matthew S.³^ORCID,Meredig Bryce²^ORCID,Viswanathan Venkatasubramanian¹^ORCID

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA 15213, USA

2. Citrine Informatics, Redwood City, CA 94063, USA

3. Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Abstract

A combination of task automation, calculation runtime improvements, machine learning surrogatization, and sequential learning-guided candidate selection within a closed-loop computational workflow can accelerate materials discovery by up to 20×.

Funder

Natural Sciences and Engineering Research Council of Canada

Advanced Research Projects Agency – Energy

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00133K

Reference65 articles.

1. How Machine Learning Will Revolutionize Electrochemical Sciences

2. Electrification and Decarbonization of the Chemical Industry

3. Challenges and prospects in the catalysis of electroreduction of nitrogen to ammonia

4. Electrochemical nitrogen reduction: recent progress and prospects

5. Machine-learned metrics for predicting the likelihood of success in materials discovery

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