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

arXiv: https://arxiv.org/abs/2504.06525 · HTML: https://mlsanity.com/paper/2504.06525/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview](https://mlsanity.com/paper/2206.07438/) (2022)
- [A survey on multi-objective hyperparameter optimization algorithms for Machine Learning](https://mlsanity.com/paper/2111.13755/) (2021)
- [Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation](https://mlsanity.com/paper/2405.12300/) (2024)
- [Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS](https://mlsanity.com/paper/2404.07381/) (2024)
- [Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data](https://mlsanity.com/paper/2411.12612/) (2024)
- [Physics-based reward driven image analysis in microscopy](https://mlsanity.com/paper/2404.14146/) (2024)
- [Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments](https://mlsanity.com/paper/2409.12462/) (2024)
- [Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries](https://mlsanity.com/paper/2412.18067/) (2024)
- [Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy](https://mlsanity.com/paper/2408.04055/) (2024)

## Built on by (most cited first, 2 of 2)

- [Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders](https://mlsanity.com/paper/2510.00243/) (2025)
- [CienaLLM: Generative Climate-Impact Extraction from News Articles with Autoregressive LLMs](https://mlsanity.com/paper/2512.19305/) (2025)

## Abstract (arXiv)

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.