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Pareto-Optimal Experimentation: Human-Guided Multi-Objective Bayesian Optimization in Scanning Probe Microscopy.

Yu Liu1, Sergei V Kalinin1,2

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Summary
This summary is machine-generated.

Multi-objective Bayesian optimization (MOBO) balances competing goals in automated experiments. This framework integrates human guidance for reproducible, efficient scientific discovery in self-driving laboratories.

Keywords:
SPMautomated experimenthuman-in-the-loop controlmulti-objective Bayesian optimizationreward engineering

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Area of Science:

  • Artificial Intelligence
  • Chemistry
  • Materials Science
  • Robotics
  • Scientific Methodology

Background:

  • Automated experimentation accelerates discovery but struggles with optimizing conflicting objectives under uncertainty.
  • Systematic methods are crucial for managing complex, multi-objective experimental landscapes.

Purpose of the Study:

  • Introduce multi-objective Bayesian optimization (MOBO) as a general framework for autonomous experimentation.
  • Enable balancing competing rewards and integrating human guidance in scientific optimization.
  • Provide a principled approach to explore parameter space and make quantifiable decisions.

Main Methods:

  • Developed a multi-objective Bayesian optimization (MOBO) framework.
  • Constructed the Pareto front to represent all trade-off solutions.
  • Integrated human-in-the-loop control for adjusting objectives and reference points.

Main Results:

  • MOBO identifies the Pareto front, detailing trade-offs between partially known reward functions.
  • Revealed interdependencies between objectives, enabling systematic exploration.
  • Demonstrated MOBO's capability to incorporate expert judgment without halting automation.

Conclusions:

  • MOBO transforms experimental optimization from trial-and-error to a reproducible, interpretable process.
  • Human guidance enhances MOBO, allowing researchers to steer experiments toward desired outcomes.
  • MOBO offers a scalable methodology for efficient, trustworthy self-driving laboratories.