Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes.
Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes.
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DOI：
10.1021/acs.oprd.5b00210
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发表时间：
2015-08-21
影响因子：
3.4
通讯作者：
Lapkin AA
中科院分区：
文献类型：
--
作者：
Houben C;Peremezhney N;Zubov A;Kosek J;Lapkin AA
化学反应的自我优化可以更快地优化反应条件或发现具有所需目标性质的分子。自优化技术已经扩展到发现制造复杂功能产品的新工艺配方。一种新的机器学习算法指导着发现过程，该算法专门为多目标目标优化设计，明确的目标是最大限度地减少“昂贵的”实验数量。这种“黑箱”方法假定没有化学体系的先验知识，因此特别适合于快速开发生产专门的小批量、高价值产品的工艺。该方法在半间歇乳液共聚合工艺配方的发现中得到了演示，目标是特定的颗粒大小和完全转化率。
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.