从最小粘度角度重新审视林德曼准则

IF 1.5 4区 物理与天体物理 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Modern Physics C Pub Date : 2024-01-11 DOI:10.1142/s0129183124501092
Pablo G. Tello, Sauro Succi
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引用次数: 0

摘要

根据最近建立的最小粘度公式重新审视了林德曼准则,发现了对熔化过程的有趣见解。该方法表明,在考虑无缺陷晶体结构时,熔化涉及电子静止能与温度升高引起的热能之间的竞争。然而,当考虑到空位在熔化过程中的作用时,分析表明关键的竞争因素是热能和空位迁移所需的能量。在这种情况下,林德曼常数的估计值接近于报告值,也接近于最近研究玻恩熔化标准和林德曼熔化标准之间相关性的模拟值。
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Revisiting the Lindemann's criterion from a minimal viscosity perspective
A revisiting of the Lindemann criterion under the recently established minimal viscosity formulation is proposed which uncovers intriguing insights into the melting process. The approach suggests that melting involves competition between the electron rest energy and the thermal one caused by temperature increase when considering a defect-free crystal structure. However, when accounting for the role of vacancies in the melting process, the analysis suggests that the key competing factors are the thermal energy and the one needed for vacancy migration. The estimated value of the Lindemann constant, in this case, is close to the reported values and with recent simulations studying the correlation between Born and Lindemann melting criteria.
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来源期刊
International Journal of Modern Physics C
International Journal of Modern Physics C 物理-计算机:跨学科应用
CiteScore
3.00
自引率
15.80%
发文量
158
审稿时长
4 months
期刊介绍: International Journal of Modern Physics C (IJMPC) is a journal dedicated to Computational Physics and aims at publishing both review and research articles on the use of computers to advance knowledge in physical sciences and the use of physical analogies in computation. Topics covered include: algorithms; computational biophysics; computational fluid dynamics; statistical physics; complex systems; computer and information science; condensed matter physics, materials science; socio- and econophysics; data analysis and computation in experimental physics; environmental physics; traffic modelling; physical computation including neural nets, cellular automata and genetic algorithms.
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