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 dec 2023
Accepted
26 feb 2024
First published
11 mar 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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