The application of machine learning (ML) to problems in homogeneous catalysis has emerged as a promising avenue for catalyst optimization. An important aspect of such optimization campaigns is determining which reactions to run at the outset of experimentation and which future predictions are the most reliable. Herein, we explore methods for these two tasks in the context of our previously developed chemoinformatics workflow. First, different methods for training set selection for library-based optimization problems are compared, including algorithmic selection and selection informed by unsupervised learning methods. Next, an array of different metrics for assessment of prediction confidence are examined in multiple catalyst manifolds. These approaches will inform future computer-guided studies to accelerate catalyst selection and reaction optimization. Finally, this work demonstrates the generality of the average steric occupancy (ASO) and average electronic indicator field (AEIF) descriptors in their application to transition metal catalysts for the first time.

中文翻译：

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用于催化剂设计的训练集选择和错误评估的计算方法：决定首先进行哪些反应以及接下来进行哪些反应的指南

机器学习（ML）在均相催化问题中的应用已成为催化剂优化的有前途的途径。这种优化活动的一个重要方面是确定在实验开始时要进行哪些反应以及最可靠的未来预测。本文中，我们在先前开发的化学信息学工作流程的背景下探索了这两个任务的方法。首先，比较了针对基于库的优化问题的训练集选择的不同方法，包括算法选择和无监督学习方法提供的选择。接下来，在多个催化剂歧管中检查用于评估预测置信度的一系列不同度量。这些方法将为未来的计算机指导研究提供参考，以加速催化剂的选择和反应的优化。最后，这项工作首次证明了平均空间占有率（ASO）和平均电子指示剂字段（AEIF）描述符在将它们应用于过渡金属催化剂中的一般性。