We have developed a Bayesian optimization (BO) workflow that integrates intra-step noise optimization into automated experimental cycles. Traditional BO approaches in automated experiments focus on optimizing experimental trajectories but often overlook the impact of measurement noise on data quality and cost. Our proposed framework simultaneously optimizes both the target property and the associated measurement noise by introducing time as an additional input parameter, thereby balancing the signal-to-noise ratio and experimental duration. Two approaches are explored: a reward-driven noise optimization and a double-optimization acquisition function, both enhancing the efficiency of automated workflows by considering noise and cost within the optimization process. We validate our method through simulations and real-world experiments using Piezoresponse Force Microscopy (PFM), demonstrating the successful optimization of measurement duration and property exploration. Our approach offers a scalable solution for optimizing multiple variables in automated experimental workflows, improving data quality, and reducing resource expenditure in materials science and beyond.

翻译：我们开发了一种贝利优化工作流程，将步骤内噪声优化集成至自动化实验循环中。传统自动化实验中的贝利优化方法主要聚焦于优化实验轨迹，但往往忽视测量噪声对数据质量和成本的影响。本框架通过引入时间作为附加输入参数，同步优化目标性能与相关测量噪声，从而平衡信噪比与实验时长。我们探索了两种实现路径：基于奖励驱动的噪声优化策略和双重优化采集函数，二者均通过在优化过程中综合考虑噪声与成本因素，提升了自动化工作流程的效率。我们通过压电力显微镜的仿真实验与真实实验验证了该方法的有效性，成功实现了测量时长与性能探索的协同优化。本方法为自动化实验工作流程中多变量优化提供了可扩展的解决方案，在提升数据质量的同时降低了材料科学等领域的资源消耗。