The optimization of chemical reactions can be laborious, particularly in the case of complex, multicomponent catalytic cycles. Now, it is shown that Bayesian optimization methods, underpinned by explainable computational physical models, can guide chemists to discover efficient organic molecular metallophotocatalyst formulations, avoiding the use of precious metals such as iridium.

中文翻译：

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用于优化多组分反应的物理编码贝叶斯助手


化学反应的优化可能很费力，特别是在复杂的多组分催化循环的情况下。现在，研究表明，以可解释的计算物理模型为基础的贝叶斯优化方法可以指导化学家发现有效的有机分子金属光催化剂配方，避免使用铱等贵金属。