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.

中文翻译：

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均相催化反应的数字帕累托前沿图

我们报告了一个数字框架，用于通过自主实验和贝叶斯优化（BO）加速探索和优化基于过渡金属的均相催化反应。具体来说，我们利用深度神经网络构建的机器学习模型进行铑催化加氢甲酰化反应，以研究 BO 超参数（包括采集函数和采样大小）对反应 Pareto 前沿映射效率的作用。