Issue 4, 2024

Digital Pareto-front mapping of homogeneous catalytic reactions

Abstract

We report a digital framework for accelerated exploration and optimization of transition metal-based homogeneous catalytic reactions through autonomous experimentation and Bayesian optimization (BO). Specifically, we utilize a machine learning model constructed with deep neural networks for a rhodium-catalyzed hydroformylation reaction to investigate the role of BO hyperparameters, including the acquisition function and sampling size, on the efficiency of reaction Pareto-front mapping.

Graphical abstract: Digital Pareto-front mapping of homogeneous catalytic reactions

Supplementary files

Article information

Article type
Communication
Submitted
13 12 2023
Accepted
26 2 2024
First published
11 3 2024
This article is Open Access
Creative Commons BY-NC license

React. Chem. Eng., 2024,9, 787-794

Digital Pareto-front mapping of homogeneous catalytic reactions

N. Orouji, J. A. Bennett, S. Sadeghi and M. Abolhasani, React. Chem. Eng., 2024, 9, 787 DOI: 10.1039/D3RE00673E

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