Work

BiDir-GRCO: A Bidirectional General Reaction Conditions Optimization Framework Integrating Multi-Armed Bandit and Regression Model

Year: 2025

Type: article

Abstract: In the research and industrial production of chemical synthesis, identifying suitable general reaction conditions is critical. However, chemical reactions typically involve multiple factors, including catalysts, solvents, temperature, and reaction time, and the optimal conditions for a single substrate are often not applicable to others. To address this limitation, this study proposes a bidirectional general reaction condition optimization framework that integrates the multiarmed bandit algorithm and regression model. The framework first utilizes the multiarmed bandit algorithm to dynamically balance exploration and exploitation in reaction condition selection. Then, a regression model, combined with molecular representation and a per-substrate selective model training strategy, is used to select substrates, thereby improving the accuracy of the general reaction condition optimization. Experimental results demonstrate that this framework exhibits high efficiency and strong adaptability across diverse reaction data sets. In two data sets with comparable numbers of substrates and reaction conditions, the framework achieves accuracy improvements of 20% and 15% over state-of-the-art models. Furthermore, the framework maintains robust optimization performance in two specialized data setsone featuring extensive substrate combinations and the other containing numerous condition combinationsfurther validating its effectiveness. We propose a bidirectional general reaction condition optimization framework that integrates the multiarmed bandit algorithm and regression model. The framework first utilizes the multiarmed bandit algorithm to dynamically balance exploration and exploitation in reaction condition selection. Then, a regression model, combined with molecular representation and a per-substrate selective model training strategy, is used to select substrates, thereby improving the accuracy of the general reaction condition optimization. Experimental results demonstrate that this framework achieves high efficiency and strong adaptability across diverse reaction data sets.

Source: ACS Omega

DOI: 10.1021/acsomega.5c06476

Authors: Quan Jiang, Mengyang Tian, Jianmin Liu, Jiawei Mo

Institutions: Guangxi Minzu University, Guangxi University of Finance and Economics, Liuzhou Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,935

Funders: Guangxi University of Finance and Economics, Guangxi University