화학공학소재연구정보센터
Chemistry Letters, Vol.48, No.8, 961-964, 2019
Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables
We examined a virtual simulation scheme for reaction condition optimization using machine learning for a small number of experiments with nine reaction conditions, consisting of five continuous and four discrete variables. Simulations were performed for predicting product yields in a synthetic reaction of tetrasilabicyclo[1.1.0]but-1(3)-ene (SiBBE). The performances in terms of accuracy and efficiency in the simulations and the chemical implications of the results were discussed.