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Neural network modeling and simulation of the synthesis of CuInS 2 /ZnS quantum dots
The development of recipes for synthesis of quantum dots (QDs), a novel semiconductor material for application in optoelectronic devices, is currently purely based on experiments. Since the quality of QDs represented by quantum yield (QY) and emission peak strongly depends on a number of different parameters (route, precursors, conditions, etc), a large number of experiments is necessary. In this article, we show that data-driven modeling can be used as a supporting tool for optimization and adoi:10.1002/eng2.12122 fatcat:7seif7ut4ncffkqnpel4eztjxa