Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation DOI Creative Commons
Xinran Chen, Zijing Zhang, Xin Hong

и другие.

Nature Synthesis, Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Язык: Английский

Hydrophosphorylation of electron-deficient alkenes and alkynes mediated by convergent paired electrolysis DOI

Xue Sun,

Jianjing Yang, Kelu Yan

и другие.

Chemical Communications, Год журнала: 2022, Номер 58(59), С. 8238 - 8241

Опубликована: Янв. 1, 2022

A straightforward and practical strategy for hydrophosphorylation of electron-deficient alkenes alkynes to access γ-ketophosphine oxides, enabled by CPE in the absence a metal, base, redox reagent, has been described.

Язык: Английский

Процитировано

19
Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization DOI
Li‐Cheng Yang, Xin Li, Shuo‐Qing Zhang

и другие.

Organic Chemistry Frontiers, Год журнала: 2021, Номер 8(22), С. 6187 - 6195

Опубликована: Янв. 1, 2021

DFT-computed structure–activity relationship data and physical organic descriptors create accurate machine learning model for HAT barrier prediction in photoredox-mediated catalysis.

Язык: Английский

Процитировано

22
High-Throughput Experimentation for Electrochemistry DOI
Jonas Rein, Song Lin, Dipannita Kalyani

и другие.

ACS symposium series, Год журнала: 2022, Номер unknown, С. 167 - 187

Опубликована: Ноя. 15, 2022

ADVERTISEMENT RETURN TO BOOKPREVChapterNEXTHigh-Throughput Experimentation for ElectrochemistryJonas ReinJonas Rein Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United StatesMore by Jonas Rein, Song Lin*Song Lin States*Email: [email protected]More Lin, Dipannita Kalyani*Dipannita Kalyani Discovery Chemistry, Merck & Co., Inc., Kenilworth, Jersey 07033, Kalyani, Dan Lehnherr*Dan Lehnherr Process Research Development, Rahway, 07065, LehnherrDOI: 10.1021/bk-2022-1419.ch010Publication Date (Web):November 15, 2022Publication History Published online15 November 2022RIGHTS PERMISSIONSThe Power High-Throughput Experimentation: General Topics Enabling Technologies Synthesis Catalysis (Volume 1)Chapter 10pp 167-187ACS Symposium SeriesVol. 1419ISBN13: 9780841297579eISBN: 9780841297562 Copyright © 2022 American SocietyChapter Views171Citations-LEARN ABOUT THESE METRICSChapter Views are the COUNTER-compliant sum full text article downloads since 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated to reflect usage leading up last few days.Citations number other articles citing this article, calculated Crossref daily. Find more information about citation counts. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InReddit Read OnlinePDF (6 MB) SUBJECTS:Electrodes,Electrolysis,Electrosynthesis,Materials,Redox reactions Get e-Alerts

Язык: Английский

Процитировано

16
Development of machine learning models for the prediction of laminar flame speeds of hydrocarbon and oxygenated fuels DOI
Zhongyu Wan, Quan‐De Wang, Bi-Yao Wang

и другие.

Fuel Communications, Год журнала: 2022, Номер 12, С. 100071 - 100071

Опубликована: Июль 3, 2022

Язык: Английский

Процитировано

15
Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation DOI Creative Commons
Xinran Chen, Zijing Zhang, Xin Hong

и другие.

Nature Synthesis, Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0