Bernhard Ramsauer, Johannes J. Cartus, Oliver T. Hofmann
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Additionally, we assess the performance of the machine learning algorithm by benchmarking it within a simulated stochastic environment.</p></div><div><h3>PROGRAM SUMMARY</h3><p>Program title: MAM-STM</p><p>CPC Library link to program files: (to be added by Technical Editor)</p><p>Developer's repository link: https://gitlab.tugraz.at/software_public/mam_stm.git</p><p>Code Ocean capsule: (to be added by Technical Editor)</p><p>Licensing provisions: GNU General Public License 3 (GPL)</p><p>Programming language: Python 3</p><p>Nature of problem: Achieving precise control over the arrangement of individual molecules on surfaces is essential for advancing nanofabrication and understanding molecular interaction processes. While self-assembly offers a method for forming nanostructures, achieving arbitrary arrangements of moieties remains difficult. Current approaches, such as scanning probe microscopy (SPM), require extensive manual intervention and precise control is difficult to achieve consistently due to the stochastic nature of quantum mechanical systems at the nanoscale. Thus, learning to manipulate several moieties in order to build even relatively small structures is challenging and time consuming and the automation through conventional expert systems is hindered by the lack of prior knowledge about the surface-moiety interaction processes.</p><p>Solution method: This scenario is ideal for machine learning algorithms, like reinforcement learning (RL), which do not require an underlying model but are able to autonomously learn the optimal manipulation parameters by performing manipulations directly at the machine. Introducing MAM-STM, which stands for Molecular and Atomic Manipulation via Scanning Tunneling Microscopy. MAM-STM allows to control molecules and atoms by learning the manipulation parameters for either vertical or lateral manipulations. However, the vast number of manipulation parameter combinations and the inefficient learning procedure of RL agents exhibit several challenges. MAM-STM overcomes these challenges with an autonomous masking routine that eliminates manipulation parameters that induce structural changes to the moiety or lift it off the surface. Additionally, a sophisticated Q-learning approach is developed that speeds up the learning procedure, enabling molecular manipulations within one day of training.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0010465524001875/pdfft?md5=74c3002522a3586528e738564a9ff30d&pid=1-s2.0-S0010465524001875-main.pdf","citationCount":"0","resultStr":"{\"title\":\"MAM-STM: A software for autonomous control of single moieties towards specific surface positions\",\"authors\":\"Bernhard Ramsauer, Johannes J. Cartus, Oliver T. 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Additionally, we assess the performance of the machine learning algorithm by benchmarking it within a simulated stochastic environment.</p></div><div><h3>PROGRAM SUMMARY</h3><p>Program title: MAM-STM</p><p>CPC Library link to program files: (to be added by Technical Editor)</p><p>Developer's repository link: https://gitlab.tugraz.at/software_public/mam_stm.git</p><p>Code Ocean capsule: (to be added by Technical Editor)</p><p>Licensing provisions: GNU General Public License 3 (GPL)</p><p>Programming language: Python 3</p><p>Nature of problem: Achieving precise control over the arrangement of individual molecules on surfaces is essential for advancing nanofabrication and understanding molecular interaction processes. While self-assembly offers a method for forming nanostructures, achieving arbitrary arrangements of moieties remains difficult. 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MAM-STM: A software for autonomous control of single moieties towards specific surface positions
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.
PROGRAM SUMMARY
Program title: MAM-STM
CPC Library link to program files: (to be added by Technical Editor)
Code Ocean capsule: (to be added by Technical Editor)
Licensing provisions: GNU General Public License 3 (GPL)
Programming language: Python 3
Nature of problem: Achieving precise control over the arrangement of individual molecules on surfaces is essential for advancing nanofabrication and understanding molecular interaction processes. While self-assembly offers a method for forming nanostructures, achieving arbitrary arrangements of moieties remains difficult. Current approaches, such as scanning probe microscopy (SPM), require extensive manual intervention and precise control is difficult to achieve consistently due to the stochastic nature of quantum mechanical systems at the nanoscale. Thus, learning to manipulate several moieties in order to build even relatively small structures is challenging and time consuming and the automation through conventional expert systems is hindered by the lack of prior knowledge about the surface-moiety interaction processes.
Solution method: This scenario is ideal for machine learning algorithms, like reinforcement learning (RL), which do not require an underlying model but are able to autonomously learn the optimal manipulation parameters by performing manipulations directly at the machine. Introducing MAM-STM, which stands for Molecular and Atomic Manipulation via Scanning Tunneling Microscopy. MAM-STM allows to control molecules and atoms by learning the manipulation parameters for either vertical or lateral manipulations. However, the vast number of manipulation parameter combinations and the inefficient learning procedure of RL agents exhibit several challenges. MAM-STM overcomes these challenges with an autonomous masking routine that eliminates manipulation parameters that induce structural changes to the moiety or lift it off the surface. Additionally, a sophisticated Q-learning approach is developed that speeds up the learning procedure, enabling molecular manipulations within one day of training.
期刊介绍:
The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper.
Computer Programs in Physics (CPiP)
These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged.
Computational Physics Papers (CP)
These are research papers in, but are not limited to, the following themes across computational physics and related disciplines.
mathematical and numerical methods and algorithms;
computational models including those associated with the design, control and analysis of experiments; and
algebraic computation.
Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.