Virtual reaction condition optimization based on machine learning for a small number of experiments in high-dimensional continuous and discrete variables

Mikito Fujinami, Junji Seino, Takumi Nukazawa, Shintaro Ishida, Takeaki Iwamoto, Hiromi Nakai*

*この研究の対応する著者

研究成果: Article査読

5 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)961-964
ページ数4
ジャーナルChemistry Letters
48
8
DOI
出版ステータスPublished - 2019

ASJC Scopus subject areas

  • 化学 (全般)

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