# Autonomous robotic nanofabrication with reinforcement learning

arXiv: https://arxiv.org/abs/2002.11952 · HTML: https://mlsanity.com/paper/2002.11952/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 8 of 8)

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- [BIGDML: Towards Exact Machine Learning Force Fields for Materials](https://mlsanity.com/paper/2106.04229/) (2021)
- [Designing Composites with Target Effective Young's Modulus using Reinforcement Learning](https://mlsanity.com/paper/2110.05260/) (2021)
- [Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2506.08423/) (2025)
- [AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems](https://mlsanity.com/paper/2409.07189/) (2024)
- [Automated Construction of Artificial Lattice Structures with Designer Electronic States](https://mlsanity.com/paper/2508.02581/) (2025)
- [Generative Image Restoration and Super-Resolution using Physics-Informed Synthetic Data for Scanning Tunneling Microscopy](https://mlsanity.com/paper/2510.25921/) (2025)

## Abstract (arXiv)

The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembly. The fundamental challenges obstructing this goal are the uncontrolled variability and poor observability of atomic-scale conformations. Here, we present a strategy to work around both obstacles, and demonstrate autonomous robotic nanofabrication by manipulating single molecules. Our approach employs reinforcement learning (RL), which finds solution strategies even in the face of large uncertainty and sparse feedback. We demonstrate the potential of our RL approach by removing molecules autonomously with a scanning probe microscope from a supramolecular structure -- an exemplary task of subtractive manufacturing at the nanoscale. Our RL agent reaches an excellent performance, enabling us to automate a task which previously had to be performed by a human. We anticipate that our work opens the way towards autonomous agents for the robotic construction of functional supramolecular structures with speed, precision and perseverance beyond our current capabilities.