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• W2442885681 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." @default.
• W2442885681 created "2016-06-24" @default.
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• W2442885681 date "2015-07-31" @default.
• W2442885681 modified "2023-09-29" @default.
• W2442885681 title "Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes" @default.
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• W2442885681 doi "https://doi.org/10.1021/acs.oprd.5b00210" @default.
• W2442885681 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4579860" @default.
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