Rapid and Mild One‐Flow Synthetic Approach to Unsymmetrical Sulfamides Guided by Bayesian Optimization

Abstract

Bayesian optimization (BO) is regarded as an efficient approach that can identify optimal conditions using a restricted number of experiments. Despite demonstrated potential of BO, applications of BO‐based approaches in synthetic organic chemistry remain limited. Herein, we achieved the first rapid and mild (5.1 s, 20 °C) one‐flow synthesis of unsymmetrical sulfamides from inexpensive sulfuryl chloride. Undesired reactions were successfully suppressed and the risk in handling sulfuryl chloride was minimized by the use of micro‐flow technology. The reaction conditions producing ≥75 % yield were identified by a machine learning approach based on BO. It was demonstrated that BO produced the desired reaction conditions with a small number of experiments (19 and 10 experiments) in the entire search space (10,500 combinations of reaction conditions). Gaussian process (GP) models produced by BO provided the relationships between combinations of reaction parameters and outputs (RCRPO).

Investigation of Parameter Control for Electrocatalytic Semihydrogenation in a Proton-Exchange Membrane Reactor Utilizing Bayesian Optimization

“…Naturally, optimizing a reaction with multiple parameters requires considerable energy, time, and chemical and human resources. Thus, to minimize resource requirements, we decided to utilize Bayesian optimization for reaction optimization (Ahneman et al, 2018;Kondo et al, 2020;Sato et al, 2021;Shields et al, 2021;Sugisawa et al, 2021).…”

Machine Learning Applications for Chemical Reactions

“…Many newly released packages employ traditional ML algorithms rather than so‐called deep learning methods due to the lack of data and high costs of experimental results. The most widely adopted method is Bayesian optimization which optimally suggests next sampling points by balancing exploitation and exploration [112–114] . Bayesian optimization packages are available on almost every matured programming communities.…”

“…The most widely adopted method is Bayesian optimization which optimally suggests next sampling points by balancing exploitation and exploration. [112][113][114] Bayesian optimization packages are available on almost every matured programming communities. Here, we introduce two Python Bayesian optimization packages specialized to the chemical reaction optimization.…”

Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids

“…[18][19][20][21][22][23][24][25] Additionally, integration of analytical techniques and controls of reactions conditions produces closed-loop systems that, when paired with an optimization protocol, enable automatic selection and evaluation of new sets of reaction conditions based on prior data. 4,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] Such systems have the potential to reduce the burden of repeated manual experiments for optimization, allowing time for more creative tasks.…”