Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations
文献类型：期刊论文
| 作者 | Song HX(宋恒旭); Nguyen, Binh Duong; Govind, Kishan; Berta, Denes; Ispanovity, Peter Dusan; Legros, Marc; Sandfeld, Stefan |
| 刊名 | ACTA MATERIALIA
 |
| 出版日期 | 2025-01 |
| 卷号 | 282页码:120455 |
| 关键词 | Dislocation avalanche Deep learning High-entropy alloy In situ TEM |
| ISSN号 | 1359-6454 |
| DOI | 10.1016/j.actamat.2024.120455 |
| 英文摘要 | In situ TEM is by far the most commonly used microscopy method for imaging dislocations, i.e., line-like defects in crystalline materials. However, quantitative image analysis so far was not possible, implying that also statistical analyses were strongly limited. In this work, we created a deep learning-based digital twin of an in situ TEM straining experiment, additionally allowing to perform matching simulations. As application we extract spatio-temporal information of moving dislocations from experiments carried out on a Cantor high entropy alloy and investigate the universality class of plastic strain avalanches. We can directly observe stick- slip motionof single dislocations and compute the corresponding avalanche statistics. The distributions turn out to be scale-free, and the exponent of the power law distribution exhibits independence on the driving stress. The introduced methodology is entirely generic and has the potential to turn meso-scale TEM microscopy into a truly quantitative and reproducible approach. |
| 分类号 | 一类 |
| WOS研究方向 | Materials Science ; Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001349894500001 |
| 资助机构 | European Research Council {759 419] ; Strategic Priority Research Program of the Chinese Academy of Sciences {XDB0620101] ; National Research, Development and Innovation Fund of Hungary {NKFIH-FK-138975] ; Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund {ELTE TKP 2021-NKTA-62] ; European Union {RRF-2.3.1-21-2022-00004] ; EKOEP-24 University Excellence Scholarship Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund |
| 其他责任者 | Sandfeld S |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/97227]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 1.【Song, Hengxu】 UCAS, Sch Engn Sci, Beijing 100049, Peoples R China 2.【Song, Hengxu】 Chinese Acad Sci, Inst Mech, LNM, Beijing 100190, Peoples R China 3.【Sandfeld, Stefan】 Rhein Westfal TH Aachen, Fac Georesources & Mat Engn 5, Chair Mat Data Sci & Mat Informat, D-52056 Aachen, Germany 4.【Legros, Marc】 CNRS, CEMES, F-31055 Toulouse, France 5.【Berta, Denes & Ispanovity, Peter Dusan】 Eotvos Lorand Univ, Dept Mat Phys, Pazmany P Stny 1-A, H-1117 Budapest, Hungary 6.【Song, Hengxu & Nguyen, Binh Duong & Govind, Kishan & Ispanovity, Peter Dusan & Sandfeld, Stefan】 Forschungszentrum Julich GmbH, Inst Adv Simulat Mat Data Sci & Informat IAS 9, D-52425 Julich, Germany |
| 推荐引用方式 GB/T 7714 | Song HX,Nguyen, Binh Duong,Govind, Kishan,et al. Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations[J]. ACTA MATERIALIA,2025,282:120455. |
| APA | 宋恒旭.,Nguyen, Binh Duong.,Govind, Kishan.,Berta, Denes.,Ispanovity, Peter Dusan.,...&Sandfeld, Stefan.(2025).Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations.ACTA MATERIALIA,282,120455. |
| MLA | 宋恒旭,et al."Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations".ACTA MATERIALIA 282(2025):120455. |
入库方式： OAI收割
来源：力学研究所
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