Open Access System for Information Sharing

Login Library

 

Article
Cited 4 time in webofscience Cited 4 time in scopus
Metadata Downloads

Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis SCIE SCOPUS

Title
Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis
Authors
Mottafegh, AmirrezaAhn, Gwang-NohKim, Dong-Pyo
Date Issued
2023-03
Publisher
Royal Society of Chemistry
Abstract
Optimizing a wide range of reaction parameters, steps, and pathways is currently considered one of the most complex and challenging problems in microflow-based organic synthesis. As a novel solution, Bayesian optimization (BO) has been utilized to efficiently guide the optimized conditions of flow reactors; however, the benchmarking process for selecting the optimal model among various surrogate models remains inefficient. In this work, we report meta optimization (MO) by benchmarking multiple surrogate models in real-time without any pre-work, which is realized by evaluating the expected values obtained by the regressor used to build each surrogate model, enabling efficient optimization of reaction conditions. By the comparison of the performance of MO with that of various BOs on four datasets of different flow syntheses, it was verified that MO consistently performs the best-in-class for all emulators developed through machine learning, while the conventional BOs based on surrogate models such as the Gaussian process, random forest, neural network ensemble, and gradient boosting demonstrated varying performances from each emulator, which implies that benchmarking is required.
URI
https://oasis.postech.ac.kr/handle/2014.oak/123669
DOI
10.1039/d2lc00938b
ISSN
1473-0197
Article Type
Article
Citation
Lab on a Chip, vol. 23, no. 6, page. 1613 - 1621, 2023-03
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher

김동표KIM, DONG PYO
Dept. of Chemical Enginrg
Read more

Views & Downloads

Browse