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A parallel chemical reaction optimization method based on preference-based multi-objective expected improvement
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DOI：10.1016/j.cjche.2024.11.004.png)
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Abstract
En 中文
Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global Optimization algorithm can effectively improve the efficiency of the search for optimal chemical reaction parameters. In this paper, we propose a multi-objective populated expectation improvement criterion for providing multiple near-optimal solutions in high-throughput chemical reaction optimization. An l-NSGA2, employing the Pseudo-power transformation method, is utilized to maximize the expected improvement acquisition function, resulting in a Pareto solution set comprising multiple designs. The approximation of the cost function can be calculated by the ensemble Gaussian process model, which greatly reduces the cost of the exact Gaussian process model. The proposed optimization method was tested on a SNAr benchmark problem. The results show that compared with the previous high-throughput experimental methods, our method can reduce the number of experiments by almost half. At the same time, it theoretically enhances temporal and spatial yields while minimizing by-product formation, potentially guiding real chemical reaction optimization. (c) 2025 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press Co., Ltd. (CIP). Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Keywords:
Algorithm
Chemical reaction
Computer simulation
Efficient global optimization
Machine learning
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