Abstract
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.
Original language | English |
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Pages (from-to) | 961-964 |
Number of pages | 4 |
Journal | Chemistry Letters |
Volume | 48 |
Issue number | 8 |
DOIs | |
Publication status | Published - 2019 |
Keywords
- Data science
- Machine learning
- Reaction condition optimization
ASJC Scopus subject areas
- General Chemistry