npj:声子非谐性—分子动力学分析
海归学者发起的公益学术平台
分享信息,整合资源
交流学术,偶尔风月
来自美国伦斯勒理工学院物理、应用物理和天文学系Vincent Meunier教授领导的团队,由分子动力学(MD)推导获得了波普精确解析表达式,以及速度的简单扰动正态模式的表达式,可用来研究振动线形,包括非谐性引起的频移效应、寿命效应,以及模拟展宽。与标准提取程序相比,他们证明了简约模型(Toy model)在几乎所有情况下,所获得收敛振动特性的模拟步骤数量,至少降低了一个数量级。在50 K时,使用这两种方法可以收敛寿命,新方法将所需的模拟时间缩短了大约1.4倍。他们将推导获得的拟合函数应用于简单模型,石墨烯、六方氮化硼(hBN)和硅,以研究振动频率和寿命与模拟时间的收敛性。他们所提出的方法,在强相关系统到生物材料的各领域,都将对分子动力学声子特性的确定产生深远影响。
该文近期发表于npj Computational Materials 5: 82 (2019),英文标题与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。
Theoretical analysis of spectral lineshapes from molecular dynamics
Andrew Cupo, Damien Tristant, Kyle Rego & Vincent Meunier
Conventional methods for calculating anharmonic phonon properties are computationally expensive. To address this issue, a theoretical approach was developed for the accelerated calculation of vibrational lineshapes for spectra obtained from finite-time molecular dynamics. The method gives access to the effect of anharmonicity-induced frequency shift and lifetime, as well as simulation broadening. For a toy model we demonstrate at least an order of magnitude reduction in the number of simulation steps needed to obtain converged vibrational properties in nearly all cases considered as compared to the standard extraction procedure. The theory is also illustrated for graphene, hexagonal boron nitride, and silicon at the density functional theory level, with up to nearly a factor of 9 reduction in the required simulation time to reach convergence in the vibrational frequencies and lifetimes. In general, we expect the newly developed method to outperform the standard procedure when the anharmonicity is sufficiently weak so that well-defined renormalized phonon quasiparticles emerge. Our extension of signal analysis to material vibrations represents a state-of-the-art advance in calculating temperature-dependent phonon properties and could be implemented in computational materials discovery packages that search for thermoelectric materials for instance, since the thermal conductivity contribution to ZT depends strongly on these characteristics.
扩展阅读
本文系网易新闻·网易号“各有态度”特色内容
媒体转载联系授权请看下方
最新评论
推荐文章
作者最新文章
你可能感兴趣的文章
Copyright Disclaimer: The copyright of contents (including texts, images, videos and audios) posted above belong to the User who shared or the third-party website which the User shared from. If you found your copyright have been infringed, please send a DMCA takedown notice to [email protected]. For more detail of the source, please click on the button "Read Original Post" below. For other communications, please send to [email protected].
版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。
版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。