Competing nucleation pathways in nanocrystal formation
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910517815148544 |
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| author | Salazar, Carlos R. Kandy, Akshay Krishna Ammothum Furstoss, Jean Gromoff, Quentin Goniakowski, Jacek Lam, Julien |
| author_facet | Salazar, Carlos R. Kandy, Akshay Krishna Ammothum Furstoss, Jean Gromoff, Quentin Goniakowski, Jacek Lam, Julien |
| contents | Despite numerous efforts from numerical approaches to complement experimental measurements, several fundamental challenges have still hindered one's ability to truly provide an atomistic picture of the nucleation process in nanocrystals. Among them, our study resolves three obstacles: (1) Machine-learning force fields including long-range interactions able to capture the finesse of the underlying atomic interactions, (2) Data-driven characterization of the local ordering in a complex structural landscape associated with several crystal polymorphs and (3) Comparing results from a large range of temperatures using both brute-force and rare-event sampling. Altogether, our simulation strategy has allowed us to study zinc oxide crystallization from nano-droplet melt. Remarkably, our results show that different nucleation pathways compete depending on the investigated degree of supercooling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Competing nucleation pathways in nanocrystal formation Salazar, Carlos R. Kandy, Akshay Krishna Ammothum Furstoss, Jean Gromoff, Quentin Goniakowski, Jacek Lam, Julien Statistical Mechanics Materials Science Soft Condensed Matter Chemical Physics Computational Physics Despite numerous efforts from numerical approaches to complement experimental measurements, several fundamental challenges have still hindered one's ability to truly provide an atomistic picture of the nucleation process in nanocrystals. Among them, our study resolves three obstacles: (1) Machine-learning force fields including long-range interactions able to capture the finesse of the underlying atomic interactions, (2) Data-driven characterization of the local ordering in a complex structural landscape associated with several crystal polymorphs and (3) Comparing results from a large range of temperatures using both brute-force and rare-event sampling. Altogether, our simulation strategy has allowed us to study zinc oxide crystallization from nano-droplet melt. Remarkably, our results show that different nucleation pathways compete depending on the investigated degree of supercooling. |
| title | Competing nucleation pathways in nanocrystal formation |
| topic | Statistical Mechanics Materials Science Soft Condensed Matter Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2407.05931 |