A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

Aug 3, 2026

Machine learning-enabled process optimization is applied to ultra-fast flow chemistry for lithium–halogen exchange. A flow chemistry platform provides precise control of temperature, residence time, and stoichiometry and enables robust data collection for model training. A Bayesian multi-objective optimization method, TSEMO (Thompson sampling efficient multi-objective optimization), is used to tune process parameters while balancing yield and impurity across campaigns with different mixing intensifications, comparing capillary and microchip reactors. Gaussian-process surrogate models further support inference of mixing-controlled versus reaction-controlled regimes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Background on ultra-fast lithium–halogen exchange

Role of flow chemistry for fast, exothermic reactions

Limitations of classical optimization methods

Motivation for machine learning workflows

Reaction Chemistry & Engineering | PAPER | | --- | | | Cite this: DOI: 10. 1039/d3re00539a Received 13th October 2023, Accepted 22nd November 2023 DOI: 10.1039/d3re00539a[rsc.li/reaction-engineering](rsc.li/reaction-engineering) A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics† Dogancan Karan,a Guoying Chen,a Nicholas Jose,abc Jiaru Bai, b Paul McDaidd and Alexei A. Lapkin *abe Discovering the optimum process parameters of ultra-fast reactions, such as lithium–halogen exchange reactions, is typically achieved by time and resource inefficient methods including one factor at a time optimization (OFAT) or classical factorial design of experiments (DoE) . Herein, we demonstrate the development of a machine learning workflow coupled with a flow chemistry platform for the optimization of the reaction conditions of a lithium–halogen exchange reaction. Flow chemistry platform allowed us to precisely control the process parameters (temperature, residence time and stoichiometry) and enabled robust and reliable data collection to train a machine learning algorithm. A Bayesian multi-objective optimization algorithm TSEMO (Thompson sampling efficient multi-objective optimization) was used to optimize the process parameters and to build process knowledge for different optimization campaigns with different mixing intensifications (capillary reactor vs. microchip reactor) . The algorithm successfully identified a set of optimal conditions corresponding the trade-off between yield and impurity in different optimization campaigns. Furthermore, the optimization results and Gaussian process (GP) surrogate models within TSEMO were further analyzed to infer the operating regime of the system for different mixing intensifications (mixing controlled vs. reaction-controlled regime) . The machine learning workflow has proven to be robust and data efficient, revealing rich information about the reaction studied compared to single-objective, OFAT and DoE approaches. 1. Introduction Organolithium compounds, such as aryllithiums are powerful tools in organic synthesis due to their high reactivity and versatility.1,2 The reaction between an aryl halide and alkyl lithium is extremely fast and highly exothermic; the resulting aryllithium intermediates have short lifetimes. The reaction time scale is usually of the order of mixing time which makes these reactions difficult to control and study. When batch processing is employed, such reactions are carried out at cryogenic conditions (below −70 °C) to avoid side reactions a Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower \#05-05, Singapore 138602, Singapore b Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK. E-mail: [aal35@cam.ac.uk](aal35@cam.ac.uk) c Accelerated Materials Ltd, 71-75, Shelton Street, WC2H 9JK London, UK d Pfizer Process Development Centre, RCMF, Shanbally, Ringaskiddy, P43 X336, Cork, Ireland e Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge CB2 1EW, UK † Electronic supplementary information (ESI) available. See DOI: [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1039/d3re00539a](10.1039/d3re00539a) and to prolong the lifetime of unstable aryllithium intermediates.3 However, poor mixing characteristics of batch processing can result in hotspot formation leading to formation of byproducts and low yields when highly reactive functional groups are present.4 Continuous flow reactors offer a viable solution to handle fast, exothermic reactions with unstable intermediates.5,6 Flow reactors offer superior heat and mass transport properties due to high surface area to volume ratio which prevents the hot spot formation and large temperature gradients in highly exothermic reactions. In addition, precise

FAQs

为什么超快的锂-卤素交换反应难以在传统条件下优化？

反应极快且高度放热，形成的中间体寿命很短，反应时间尺度往往与混合时间相当，因此控制与研究效率较低。

该研究如何利用流动化学平台获得可用于机器学习的数据？

平台可精确调控温度、停留时间和当量（stoichiometry），并实现可靠、稳健的数据采集，从而支撑机器学习算法训练与优化。

TSEMO在优化中扮演什么角色，如何处理多目标权衡？

TSEMO（基于Thompson sampling的高效多目标贝叶斯优化）同时考虑多个指标，成功找到在不同混合强度（毛细管与微芯片）下满足产率与杂质之间权衡的最优条件。

Aug 3, 2026

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