<ul><li>Automated experimentation has the potential to revolutionize scientific discovery, but its effectiveness depends on well-defined optimization targets.</li><li>Multi-Objective Bayesian Optimization (MOBO) is applied to balance multiple, competing rewards in autonomous experimentation.</li><li>Using scanning probe microscopy (SPM) imaging, MOBO optimizes imaging parameters to enhance measurement quality, reproducibility, and efficiency.</li><li>MOBO offers a natural framework for human-in-the-loop decision-making, enabling researchers to fine-tune experimental trade-offs based on domain expertise.</li></ul>

The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy

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