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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.09694 |
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| _version_ | 1866911840289685504 |
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| author | Ramsauer, Bernhard Cartus, Johannes J. Hofmann, Oliver T. |
| author_facet | Ramsauer, Bernhard Cartus, Johannes J. Hofmann, Oliver T. |
| contents | In this publication we introduce MAM-STM, a software to autonomously manipulate arbitrary moieties towards specific positions on a metal surface utilizing the tip of a scanning tunneling microscope (STM). Finding the optimal manipulation parameters for a specific moiety is challenging and time consuming, even for human experts. MAM-STM combines autonomous data acquisition with a sophisticated Q-learning implementation to determine the optimal bias voltage, the z-approach distance, and the tip position relative to the moiety. This then allows to arrange single molecules and atoms at will. In this work, we provide a tutorial based on a simulated response to offer a comprehensive explanation on how to use and customize MAM-STM. Additionally, we assess the performance of the machine learning algorithm by benchmarking it within a simulated stochastic environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09694 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAM-STM: A software for autonomous control of single moieties towards specific surface positions Ramsauer, Bernhard Cartus, Johannes J. Hofmann, Oliver T. Applied Physics In this publication we introduce MAM-STM, a software to autonomously manipulate arbitrary moieties towards specific positions on a metal surface utilizing the tip of a scanning tunneling microscope (STM). Finding the optimal manipulation parameters for a specific moiety is challenging and time consuming, even for human experts. MAM-STM combines autonomous data acquisition with a sophisticated Q-learning implementation to determine the optimal bias voltage, the z-approach distance, and the tip position relative to the moiety. This then allows to arrange single molecules and atoms at will. In this work, we provide a tutorial based on a simulated response to offer a comprehensive explanation on how to use and customize MAM-STM. Additionally, we assess the performance of the machine learning algorithm by benchmarking it within a simulated stochastic environment. |
| title | MAM-STM: A software for autonomous control of single moieties towards specific surface positions |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2404.09694 |