The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy
This work addresses the challenge of defining optimization targets in autonomous scientific discovery for researchers in scanning probe microscopy, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of optimizing uncertain or probabilistic rewards in automated experimentation, specifically in scanning probe microscopy imaging, by applying Multi-Objective Bayesian Optimization (MOBO) to balance competing objectives like measurement quality, reproducibility, and efficiency, resulting in enhanced experimental outcomes.
Automated experimentation has the potential to revolutionize scientific discovery, but its effectiveness depends on well-defined optimization targets, which are often uncertain or probabilistic in real-world settings. In this work, we demonstrate the application of Multi-Objective Bayesian Optimization (MOBO) to balance multiple, competing rewards in autonomous experimentation. Using scanning probe microscopy (SPM) imaging, one of the most widely used and foundational SPM modes, we show that MOBO can optimize imaging parameters to enhance measurement quality, reproducibility, and efficiency. A key advantage of this approach is the ability to compute and analyze the Pareto front, which not only guides optimization but also provides physical insights into the trade-offs between different objectives. Additionally, MOBO offers a natural framework for human-in-the-loop decision-making, enabling researchers to fine-tune experimental trade-offs based on domain expertise. By standardizing high-quality, reproducible measurements and integrating human input into AI-driven optimization, this work highlights MOBO as a powerful tool for advancing autonomous scientific discovery.