Automatic discovery and optimization of chemical processes

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.This work was in part funded by EPSRC project “Closed Loop Optimization for Sustainable Chemical Manufacture” [EP/L003309/1].This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.coche.2015.07.00