将测量噪声纳入优化，提升实验效率与数据质量。
Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments
- 把时间作为输入参数，同步优化实验性能与测量噪声。
- 在真实原子力显微镜实验中缩短测量时长并提升信号质量。
- 适合材料科学等需高精度自动化实验的研究者。
我们提出了一种贝叶斯优化（BO）工作流，将步骤内噪声优化集成到自动化实验循环中。传统自动化实验的贝叶斯优化仅关注实验轨迹优化，常忽略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额外输入参数，同时优化目标属性和关联测量噪声，平衡信噪比与实验时长。研究探索了两种方法：基于奖励的噪声优化和双优化采集函数，均通过在优化过程中考虑噪声与成本，提升了自动化流程效率。通过模拟和实际压电力显微镜（Piezoresponse Force Microscopy, PFM）实验验证，成功实现了测量时长优化与性能探索。该方法为自动化实验中多变量协同优化提供了可扩展方案，提升了数据质量并降低了资源消耗。
原文摘要 · Abstract (English)
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.
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