Issue 6, 2023

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

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.

Graphical abstract: Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis

Supplementary files

Article information

Article type
Paper
Submitted
06 oct. 2022
Accepted
18 janv. 2023
First published
18 janv. 2023

Lab Chip, 2023,23, 1613-1621

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

A. Mottafegh, G. Ahn and D. Kim, Lab Chip, 2023, 23, 1613 DOI: 10.1039/D2LC00938B

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