用多目标优化提升扫描探针显微镜的成像质量与效率
The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy
- 采用多目标贝叶斯优化平衡多个竞争性指标
- 通过帕累托前沿分析实现参数自适应优化
- 适合需要人机协同的复杂实验场景
自动化实验有望革新科学发现，但其效果依赖于明确的优化目标，而真实场景中目标常具不确定性和概率性。本文将多目标贝叶斯优化（MOBO）应用于扫描探针显微镜（SPM）成像，展示其在优化成像参数以提升测量质量、可重复性和效率方面的潜力。该方法的核心优势在于计算并分析帕累托前沿，不仅指导优化过程，还能揭示不同目标间的物理权衡关系。此外，MOBO为人类参与决策提供了自然框架，使研究者可根据领域知识调整实验权衡。通过标准化高质量、可复现的测量并融合人为输入，本工作凸显了MOBO在推动自主科学发现中的强大作用。
原文摘要 · Abstract (English)
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.
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