Automatic discovery and optimization of chemical processes
Automatic discovery and optimization of chemical processes
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DOI：
10.1016/j.coche.2015.07.001
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发表时间：
2015-08-01
影响因子：
6.6
通讯作者：
Lapkin, Alexei A.
中科院分区：
文献类型：
--
作者：
Houben, Claudia;Lapkin, Alexei A.
本文介绍了第一个概述的技术和方法，使闭环优化，有时也称为“自我优化”，以及在不同领域的分子科学的发现的最新发展。闭环实验平台提供了巨大的新机会，显著提高了生产率，并能够进行全新类型的实验。这些实验涉及三个主要的使能技术领域：自动化实验系统、连接到自动化化学信息学软件的分析仪器以及优化或决策算法。我们回顾了关于机器人实验，3D打印实验室器皿，具有多个分析仪器的实验系统和基于机器学习方法的先进优化算法的最令人兴奋的发展。描述了一系列不同的化学问题，这表明了这种新兴的实验方法的潜在应用的广度。
This paper presents the first overview of recent developments in techniques and methods that enable closed-loop optimization, also sometimes called 'self optimization', as well as discovery in different areas of molecular sciences. The closed-loop experimental platforms offer tremendous new opportunities by significantly increasing productivity, as well as enabling completely new types of experiments to be performed. Such experiments involve three main enabling technology areas: automated experimental systems, analytical instruments connected to automated chemoinformatics software and optimization or decision-making algorithms. We review the most exciting developments concerning robotic experiments, 3D printed lab-ware, experimental systems with multiple analytical instruments and advanced optimization algorithms based on machine learning approaches. A range of different chemical problems is described, which show the breadth of potential applications of this emerging experimental approach.