Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning-Reference-Cited by-同舟云学术

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning

Published:2023 Issue:30 Volume:14 Page:8061-8069
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Dunlap John H.¹²^ORCID,Ethier Jeffrey G.¹²,Putnam-Neeb Amelia A.¹³^ORCID,Iyer Sanjay⁴,Luo Shao-Xiong Lennon⁵^ORCID,Feng Haosheng⁵^ORCID,Garrido Torres Jose Antonio⁶,Doyle Abigail G.⁷^ORCID,Swager Timothy M.⁵^ORCID,Vaia Richard A.¹^ORCID,Mirau Peter¹,Crouse Christopher A.¹,Baldwin Luke A.¹^ORCID

Affiliation:

1. Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH, 45433, USA

2. UES, Inc., Dayton, OH, 45431, USA

3. National Research Council Research Associate, Air Force Research Laboratory, Wright-Patterson AFB, OH, 45433, USA

4. Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA

5. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

6. Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA

7. Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, 90095, USA

Abstract

Human-in-the-loop experimentation enables interactive machine learning for continuous flow chemistry reaction planning and optimization.

Funder

Princeton University

U.S. Department of Defense

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SC/D3SC01303K

Reference62 articles.

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