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| 001 | 1041610 | ||
| 005 | 20250424202216.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2002.11952 |2 doi |
| 037 | _ | _ | |a FZJ-2025-02344 |
| 100 | 1 | _ | |a Leinen, Philipp |0 P:(DE-Juel1)164154 |b 0 |
| 245 | _ | _ | |a Autonomous robotic nanofabrication with reinforcement learning |
| 260 | _ | _ | |c 2020 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1745493949_28179 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5213 - Quantum Nanoscience (POF4-521) |0 G:(DE-HGF)POF4-5213 |c POF4-521 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Mesoscale and Nanoscale Physics (cond-mat.mes-hall) |2 Other |
| 650 | _ | 7 | |a Artificial Intelligence (cs.AI) |2 Other |
| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a Robotics (cs.RO) |2 Other |
| 650 | _ | 7 | |a FOS: Physical sciences |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Esders, Malte |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Schütt, Kristof T. |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Wagner, Christian |0 P:(DE-Juel1)140276 |b 3 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Müller, Klaus-Robert |0 P:(DE-Juel1)5055 |b 4 |e Corresponding author |
| 700 | 1 | _ | |a Tautz, F. Stefan |0 P:(DE-Juel1)128791 |b 5 |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2002.11952 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2002.11952 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1041610 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)140276 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)128791 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-521 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Quantum Materials |9 G:(DE-HGF)POF4-5213 |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-3-20110106 |k PGI-3 |l Quantum Nanoscience |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-3-20110106 |
| 980 | _ | _ | |a UNRESTRICTED |
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