Machine-Learning-Guided Discovery of Electrochemical Reactions-Reference-Cited by-同舟云学术

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Machine-Learning-Guided Discovery of Electrochemical Reactions

Published:2022-12-02 Issue:49 Volume:144 Page:22599-22610
ISSN:0002-7863
Container-title:Journal of the American Chemical Society
language:en
Short-container-title:J. Am. Chem. Soc.

Author:

Zahrt Andrew F.¹^ORCID,Mo Yiming¹²^ORCID,Nandiwale Kakasaheb Y.¹^ORCID,Shprints Ron¹^ORCID,Heid Esther¹³^ORCID,Jensen Klavs F.¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States

2. College of Chemical and Biological Engineering, Zhejiang University, Hangzhou310027, China

3. Institute of Materials Chemistry, TU Wien, Vienna1060, Austria

Funder

Defense Advanced Research Projects Agency

Austrian Science Fund

Camille and Henry Dreyfus Foundation

Arnold and Mabel Beckman Foundation

Machine Learning for Pharmaceutical Discovery and Synthesis consortium

Publisher

American Chemical Society (ACS)

Subject

Colloid and Surface Chemistry,Biochemistry,General Chemistry,Catalysis

Link

https://pubs.acs.org/doi/pdf/10.1021/jacs.2c08997

Reference48 articles.

1. Contemporary screening approaches to reaction discovery and development

2. Discovery of an α-Amino C–H Arylation Reaction Using the Strategy of Accelerated Serendipity

3. Discovery of Chemical Reactions through Multidimensional Screening

4. Tandem Processes Identified from Reaction Screening: Nucleophilic Addition to Aryl N-Phosphinylimines Employing La(III)-TFAA Activation

5. A Simple, Multidimensional Approach to High-Throughput Discovery of Catalytic Reactions

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