The related problems of chemical reaction optimization and reaction scope search concern the discovery of reaction pathways and conditions that provide the best percentage yield of a target product. The space of possible reaction pathways or conditions is too large to search in full, so identifying a globally optimal set of conditions must instead draw on mathematical methods to identify areas of the space that should be investigated. An intriguing contribution to this area of research is the recent development of the Experimental Design for Bayesian optimization (EDBO) optimizer [1]. Bayesian optimization works by building an approximation to the true function to be optimized based on a small set of simulations, and selecting the next point (or points) to be tested based on an acquisition function reflecting the value of different points within the input space. In this work, we evaluated the robustness of the EDBO optimizer under several changes to its specification. We investigated the effect on the performance of the optimizer of altering the acquisition function and batch size, applied the method to other existing reaction yield data sets, and considered its performance in the new problem domain of molecular power conversion efficiency in photovoltaic cells. Our results indicated that the EDBO optimizer broadly performs well under these changes; of particular note is the competitive performance of the computationally cheaper acquisition function Thompson Sampling when compared to the original Expected Improvement function, and some concerns around the method’s performance for “incomplete” input domains.

中文翻译：

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化学反应贝叶斯优化方法参数和问题域变化下的鲁棒性

化学反应优化和反应范围搜索的相关问题涉及发现提供目标产物最佳百分比收率的反应途径和条件。可能的反应路径或条件的空间太大而无法完全搜索，因此确定一组全局最优条件必须利用数学方法来确定应该研究的空间区域。贝叶斯优化实验设计 (EDBO) 优化器 [1] 的最新发展是对这一研究领域的一个有趣贡献。贝叶斯优化的工作原理是基于一小组模拟构建要优化的真实函数的近似值，根据反映输入空间内不同点的值的采集函数选择下一个（或多个）要测试的点。在这项工作中，我们评估了 EDBO 优化器在对其规范进行多次更改后的稳健性。我们研究了改变采集函数和批量大小对优化器性能的影响，将该方法应用于其他现有的反应产率数据集，并考虑了其在光伏电池中分子功率转换效率的新问题域中的性能。我们的结果表明 EDBO 优化器在这些变化下大体上表现良好；特别值得注意的是计算成本更低的采集函数 Thompson Sampling 与原始预期改进函数相比的竞争性能，