Zobrazeno 1 - 10
of 342
pro vyhledávání: '"Matthew S. Sigman"'
Autor:
Hongyuan Sheng, Jingwen Sun, Oliver Rodríguez, Benjamin B. Hoar, Weitong Zhang, Danlei Xiang, Tianhua Tang, Avijit Hazra, Daniel S. Min, Abigail G. Doyle, Matthew S. Sigman, Cyrille Costentin, Quanquan Gu, Joaquín Rodríguez-López, Chong Liu
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelera
Externí odkaz:
https://doaj.org/article/6a2486d0837e42c5b7f7d36140605d65
Autor:
Priyanka Raghavan, Brittany C. Haas, Madeline E. Ruos, Jules Schleinitz, Abigail G. Doyle, Sarah E. Reisman, Matthew S. Sigman, Connor W. Coley
Publikováno v:
ACS Central Science, Vol 9, Iss 12, Pp 2196-2204 (2023)
Externí odkaz:
https://doaj.org/article/6c4d5d063a7845fda184e99049e21975
Autor:
Wendy L. Williams, Lingyu Zeng, Tobias Gensch, Matthew S. Sigman, Abigail G. Doyle, Eric V. Anslyn
Publikováno v:
ACS Central Science, Vol 7, Iss 10, Pp 1622-1637 (2021)
Externí odkaz:
https://doaj.org/article/033c4aa85e0f41179468df46019f901d
Autor:
Melodie Christensen, Lars P. E. Yunker, Folarin Adedeji, Florian Häse, Loïc M. Roch, Tobias Gensch, Gabriel dos Passos Gomes, Tara Zepel, Matthew S. Sigman, Alán Aspuru-Guzik, Jason E. Hein
Publikováno v:
Communications Chemistry, Vol 4, Iss 1, Pp 1-12 (2021)
An automated closed-loop system optimizes a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm that incorporates unbiased and categorical process parameters.
Externí odkaz:
https://doaj.org/article/fc4f62943ff64484b8d72e580bfeb7ba
Publikováno v:
ACS Central Science, Vol 6, Iss 8, Pp 1241-1247 (2020)
Externí odkaz:
https://doaj.org/article/85256621bb8943eb87890ff597689a6e
Autor:
Koen H. Hendriks, Sophia G. Robinson, Miles N. Braten, Christo S. Sevov, Brett A. Helms, Matthew S. Sigman, Shelley D. Minteer, Melanie S. Sanford
Publikováno v:
ACS Central Science, Vol 4, Iss 2, Pp 189-196 (2018)
Externí odkaz:
https://doaj.org/article/1b14e4b57fe14301a65dca0e0bdd9173
Autor:
Jonas Rein, Soren D. Rozema, Olivia C. Langner, Samson B. Zacate, Melissa A. Hardy, Juno C. Siu, Brandon Q. Mercado, Matthew S. Sigman, Scott J. Miller, Song Lin
Publikováno v:
Science. 380:706-712
Catalytic enantioselective methods that are generally applicable to a broad range of substrates are rare. We report a strategy for the oxidative desymmetrization of meso -diols predicated on a nontraditional catalyst optimization protocol by using a
Publikováno v:
Journal of the American Chemical Society. 145:11781-11788
Autor:
Jordan J. Dotson, Lucy van Dijk, Jacob C. Timmerman, Samantha Grosslight, Richard C. Walroth, Francis Gosselin, Kurt Püntener, Kyle A. Mack, Matthew S. Sigman
Publikováno v:
J Am Chem Soc
Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-object
Autor:
Mohammad H. Samha, Julie L. H. Wahlman, Jacquelyne A. Read, Jacob Werth, Eric N. Jacobsen, Matthew S. Sigman
Publikováno v:
ACS Catal
Hydrogen bond-based organocatalysts rely on networks of attractive noncovalent interactions (NCIs) to impart enantioselectivity. As a specific example, aryl pyrrolidine substituted urea, thiourea, and squaramide organocatalysts function cooperatively