Precise large-scale chemical transformations on surfaces: deep learning meets scanning probe microscopy with interpretability
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913615064334336 |
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| author | Wu, Nian Aapro, Markus Jestilä, Joakim S. Drost, Robert Garcıa, Miguel Martınez Torres, Tomas Xiang, Feifei Cao, Nan He, Zhijie Bottari, Giovanni Liljeroth, Peter Foster, Adam S. |
| author_facet | Wu, Nian Aapro, Markus Jestilä, Joakim S. Drost, Robert Garcıa, Miguel Martınez Torres, Tomas Xiang, Feifei Cao, Nan He, Zhijie Bottari, Giovanni Liljeroth, Peter Foster, Adam S. |
| contents | Scanning Probe Microscopy (SPM) techniques have shown great potential in fabricating nanoscale structures endowed with exotic quantum properties achieved through various manipulations of atoms and molecules. However, precise control requires extensive domain knowledge, which is not necessarily transferable to new systems and cannot be readily extended to large-scale operations. Therefore, efficient and autonomous SPM techniques are needed to learn optimal strategies for new systems, in particular for the challenge of controlling chemical reactions and hence offering a route to precise atomic and molecular construction. In this paper, we developed a software infrastructure named AutoOSS (\textbf{Auto}nomous \textbf{O}n-\textbf{S}urface \textbf{S}ynthesis) to automate bromine removal from hundreds of Zn(II)-5,15-bis(4-bromo-2,6-dimethylphenyl)porphyrin (\ch{ZnBr2Me4DPP}) on Au(111), using neural network models to interpret STM outputs and deep reinforcement learning models to optimize manipulation parameters. This is further supported by Bayesian Optimization Structure Search (BOSS) and Density Functional Theory (DFT) computations to explore 3D structures and reaction mechanisms based on STM images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20014 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Precise large-scale chemical transformations on surfaces: deep learning meets scanning probe microscopy with interpretability Wu, Nian Aapro, Markus Jestilä, Joakim S. Drost, Robert Garcıa, Miguel Martınez Torres, Tomas Xiang, Feifei Cao, Nan He, Zhijie Bottari, Giovanni Liljeroth, Peter Foster, Adam S. Materials Science Scanning Probe Microscopy (SPM) techniques have shown great potential in fabricating nanoscale structures endowed with exotic quantum properties achieved through various manipulations of atoms and molecules. However, precise control requires extensive domain knowledge, which is not necessarily transferable to new systems and cannot be readily extended to large-scale operations. Therefore, efficient and autonomous SPM techniques are needed to learn optimal strategies for new systems, in particular for the challenge of controlling chemical reactions and hence offering a route to precise atomic and molecular construction. In this paper, we developed a software infrastructure named AutoOSS (\textbf{Auto}nomous \textbf{O}n-\textbf{S}urface \textbf{S}ynthesis) to automate bromine removal from hundreds of Zn(II)-5,15-bis(4-bromo-2,6-dimethylphenyl)porphyrin (\ch{ZnBr2Me4DPP}) on Au(111), using neural network models to interpret STM outputs and deep reinforcement learning models to optimize manipulation parameters. This is further supported by Bayesian Optimization Structure Search (BOSS) and Density Functional Theory (DFT) computations to explore 3D structures and reaction mechanisms based on STM images. |
| title | Precise large-scale chemical transformations on surfaces: deep learning meets scanning probe microscopy with interpretability |
| topic | Materials Science |
| url | https://arxiv.org/abs/2409.20014 |