Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning
Abstract Surface‐supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition. They will significantly impact the electronic/magnetic properties. Moreover, surface supported atoms are also widely explored for high active and...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Smart Molecules |
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| Online Access: | https://doi.org/10.1002/smo.20240018 |
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| _version_ | 1850275470288879616 |
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| author | Luneng Zhao Yanhan Ren Xiaoran Shi Hongsheng Liu Zhigen Yu Junfeng Gao Jijun Zhao |
| author_facet | Luneng Zhao Yanhan Ren Xiaoran Shi Hongsheng Liu Zhigen Yu Junfeng Gao Jijun Zhao |
| author_sort | Luneng Zhao |
| collection | DOAJ |
| description | Abstract Surface‐supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition. They will significantly impact the electronic/magnetic properties. Moreover, surface supported atoms are also widely explored for high active and selecting catalysts. Severe deformation, even dipping into the surface, of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces. However, most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions. Here, we develop an accurate graph neural network machine learning potential (MLP) from ab initio data by active learning architecture through fine‐tuning pre‐trained models, and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters (1–8 atoms) on perfect and various defective MoS2 monolayers. Interestingly, Mo clusters can always sink and embed themselves into MoS2 layers. In contrast, S clusters float on perfect surfaces. On the defective surface, a few S atoms will fill the vacancy and rest S clusters float on the top. Such significant structural reconstructions should be carefully taken into account. |
| format | Article |
| id | doaj-art-d47ce95a83d64a0fa41b1568f922090f |
| institution | OA Journals |
| issn | 2751-4587 2751-4595 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Smart Molecules |
| spelling | doaj-art-d47ce95a83d64a0fa41b1568f922090f2025-08-20T01:50:44ZengWileySmart Molecules2751-45872751-45952025-03-0131n/an/a10.1002/smo.20240018Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learningLuneng Zhao0Yanhan Ren1Xiaoran Shi2Hongsheng Liu3Zhigen Yu4Junfeng Gao5Jijun Zhao6State Key Laboratory of Structural Analysis for Industrial Equipment&School of Physics Dalian University of Technology Dalian ChinaState Key Laboratory of Structural Analysis for Industrial Equipment&School of Physics Dalian University of Technology Dalian ChinaState Key Laboratory of Structural Analysis for Industrial Equipment&School of Physics Dalian University of Technology Dalian ChinaState Key Laboratory of Structural Analysis for Industrial Equipment&School of Physics Dalian University of Technology Dalian ChinaInstitute of High Performance Computing (IHPC) Agency for Science, Technology and Research(A*STAR) Singapore SingaporeState Key Laboratory of Structural Analysis for Industrial Equipment&School of Physics Dalian University of Technology Dalian ChinaState Key Laboratory of Structural Analysis for Industrial Equipment&School of Physics Dalian University of Technology Dalian ChinaAbstract Surface‐supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition. They will significantly impact the electronic/magnetic properties. Moreover, surface supported atoms are also widely explored for high active and selecting catalysts. Severe deformation, even dipping into the surface, of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces. However, most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions. Here, we develop an accurate graph neural network machine learning potential (MLP) from ab initio data by active learning architecture through fine‐tuning pre‐trained models, and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters (1–8 atoms) on perfect and various defective MoS2 monolayers. Interestingly, Mo clusters can always sink and embed themselves into MoS2 layers. In contrast, S clusters float on perfect surfaces. On the defective surface, a few S atoms will fill the vacancy and rest S clusters float on the top. Such significant structural reconstructions should be carefully taken into account.https://doi.org/10.1002/smo.20240018active learningmachine learning potentialMonte Carlosurface‐supported clusters |
| spellingShingle | Luneng Zhao Yanhan Ren Xiaoran Shi Hongsheng Liu Zhigen Yu Junfeng Gao Jijun Zhao Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning Smart Molecules active learning machine learning potential Monte Carlo surface‐supported clusters |
| title | Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning |
| title_full | Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning |
| title_fullStr | Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning |
| title_full_unstemmed | Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning |
| title_short | Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2 with active machine learning |
| title_sort | unveiling the unexpected sinking and embedding dynamics of surface supported mo s clusters on 2d mos2 with active machine learning |
| topic | active learning machine learning potential Monte Carlo surface‐supported clusters |
| url | https://doi.org/10.1002/smo.20240018 |
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