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

ABSTRACT: 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.

SUBMITTER: Houben C

PROVIDER: S-EPMC4579860 | biostudies-other | 2015 Aug

REPOSITORIES: biostudies-other

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

Houben Claudia C Peremezhney Nicolai N Zubov Alexandr A Kosek Juraj J Lapkin Alexei A AA

Organic process research & development 20150730 8

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 ...[more]

PMID: 26435638

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