Automatic discovery and optimization of chemical processes

• Published in: Current opinion in chemical engineering Vol. 9; pp. 1 - 7
• Main Authors: Houben, Claudia, Lapkin, Alexei A
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2015
• ISSN: 2211-3398; 2211-3398
• DOI: 10.1016/j.coche.2015.07.001

•An overview of closed-loop experiment systems for materials discovery.•Discussion of machine-learning algorithms for target optimization in chemical systems.•An overview of analytical methodologies for data-rich experiments. 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.