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Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes

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Houben, C 
Peremezhney, N 
Zubov, A 
Kosek, J 
Lapkin, AA 


Self-optimization of chemical reactions enables faster optimization of reaction conditions or discovery of molecules with required target properties. The technology of self-optimization has been expanded to discovery of new process recipes for manufacture of complex functional products. A new machine-learning algorithm, specifically designed for multiobjective target optimization with an explicit aim to minimize the number of “expensive” experiments, guides the discovery process. This “black-box” approach assumes no a priori knowledge of chemical system and hence particularly suited to rapid development of processes to manufacture specialist low-volume, high-value products. The approach was demonstrated in discovery of process recipes for a semibatch emulsion copolymerization, targeting a specific particle size and full conversion.



self-optimized reactors, machine learning, multi-objective optimization, automated discovery, sequential optimization

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Organic Process Research & Development

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American Chemical Society
European Commission (280827)
The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (EC FP7) Grant Agreement no. [NMP2-SL-2012-280827] and EPSRC project “Closed Loop Optimization for Sustainable Chemical Manufacture” [EP/L003309/1].