硅
材料科学
原子间势
背景(考古学)
位错
分子动力学
半导体
变形(气象学)
金刚石立方
纳米尺度
晶体硅
相(物质)
化学物理
纳米技术
工程物理
凝聚态物理
钻石
计算化学
光电子学
物理
化学
复合材料
生物
量子力学
古生物学
作者
Rafal Abram,D. Chrobak,Jesper Byggmästar,K. Nordlund,Roman Nowak
出处
期刊:Materialia
[Elsevier BV]
日期:2023-03-30
卷期号:28: 101761-101761
被引量:9
标识
DOI:10.1016/j.mtla.2023.101761
摘要
In spite of remarkable developments in the field of advanced materials, silicon remains one of the foremost semiconductors of the day. Of enduring relevance to science and technology is silicon's nanomechanical behaviour including phase transformation, amorphization and dislocations generation, particularly in the context of molecular dynamics and materials research. So far, comprehensive modelling of the whole cycle of events in silicon during nanoscale deformation has not been possible, however, due to the limitations inherent in the existing interatomic potentials. This paper examines how well an unconventional combination of two well-known potentials - the Tersoff and Stillinger-Weber - can perform in simulating that complexity. Our model indicates that an irreversible deformation of silicon (Si-I) is set in motion by a transformation to a non-diamond structure (Si-nd), and followed by a subsequent transition to the Si-II and Si-XII phases (Si-1→Si-nd→Si-II→Si-XII). This leads to the generation of dislocations spreading outwards from the incubation zone. In effect, our simulations parallel the structural changes detected experimentally in the deformed material. This includes both the experimentally observed sequence of phase transitions and dislocation activity, which - taken together - neither the Tersoff nor Stillinger-Weber, or indeed any other available Si interatomic potential, is able to achieve in its own right. Notably, the Si-XII phase was not discerned by any of the previous computational models, which points towards the effectiveness of our integrated approach to forecasting novel phenomena discovered by advanced structure examinations. Last not least, our method satisfies the demand for a quick means to construct potentials by opening up the huge library of existing models to new applications in various branches of materials science.
科研通智能强力驱动
Strongly Powered by AbleSci AI