# Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation

arXiv: https://arxiv.org/abs/2405.12300 · HTML: https://mlsanity.com/paper/2405.12300/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 62 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy](https://mlsanity.com/paper/2310.05018/) (2023)
- [Autonomous robotic nanofabrication with reinforcement learning](https://mlsanity.com/paper/2002.11952/) (2020)

## Built on by (most cited first, 6 of 6)

- [Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments](https://mlsanity.com/paper/2410.02717/) (2024)
- [Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy](https://mlsanity.com/paper/2408.04055/) (2024)
- [The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy](https://mlsanity.com/paper/2504.06525/) (2025)
- [Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2603.16715/) (2026)
- [AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design](https://mlsanity.com/paper/2604.00359/) (2026)
- [From Closed-Loop Optimization to Open Decision Making: Coupled Digital Twins for Predictive and Autonomous Microscopy](https://mlsanity.com/paper/2607.05758/) (2026)

## Abstract (arXiv)

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements towards operationalization of automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer (HPC), which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.