On accelerating reaction optimization using computational Gibbs energy barriers: A numerical consideration utilizing a computational dataset

Abstract

Reaction optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools like Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted reaction optimization, this study employs a computational Gibbs energy barrier dataset in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as experimental reactivity and those with systematic and random noises as computational reactivity. The present numerical investigation shows that even the computational reactivity affected by noises as much as 20 kJ/mol helps reduce the number of required experiments.