Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
-
- Andrew F. Zahrt
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
-
- Jeremy J. Henle
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
-
- Brennan T. Rose
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
-
- Yang Wang
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
-
- William T. Darrow
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
-
- Scott E. Denmark
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
この論文をさがす
説明
<jats:title>Predicting catalyst selectivity</jats:title> <jats:p> Asymmetric catalysis is widely used in chemical research and manufacturing to access just one of two possible mirror-image products. Nonetheless, the process of tuning catalyst structure to optimize selectivity is still largely empirical. Zahrt <jats:italic>et al.</jats:italic> present a framework for more efficient, predictive optimization. As a proof of principle, they focused on a known coupling reaction of imines and thiols catalyzed by chiral phosphoric acid compounds. By modeling multiple conformations of more than 800 prospective catalysts, and then training machine-learning algorithms on a subset of experimental results, they achieved highly accurate predictions of enantioselectivities. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" related-article-type="in-this-issue" xlink:href="10.1126/science.aau5631">eaau5631</jats:related-article> </jats:p>
収録刊行物
-
- Science
-
Science 363 (6424), eaau5631-, 2019-01-18
American Association for the Advancement of Science (AAAS)