工作流程
人工神经网络
基质(水族馆)
范围(计算机科学)
计算机科学
产量(工程)
集合(抽象数据类型)
过程(计算)
人工智能
催化作用
航程(航空)
钯
机器学习
生物系统
化学
材料科学
数据库
生物化学
海洋学
冶金
复合材料
生物
程序设计语言
地质学
操作系统
作者
N. Ian Rinehart,Rakesh K. Saunthwal,Joël Wellauer,Andrew F. Zahrt,Lukas Schlemper,Alexander S. Shved,Raphael Bigler,Serena Fantasia,Scott E. Denmark
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2023-09-01
卷期号:381 (6661): 965-972
被引量:6
标识
DOI:10.1126/science.adg2114
摘要
Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)-catalyzed carbon-nitrogen (C-N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C-N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows.
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