Scalable Ehrenfest Molecular Dynamics Exploiting the Locality of Density-Functional Tight-Binding Hamiltonian

Scalable Ehrenfest Molecular Dynamics Exploiting the Locality of Density-Functional Tight-Binding Hamiltonian
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利用密度函数紧束缚哈密顿量局部性的可扩展 Ehrenfest 分子动力学

DOI:
10.1021/acs.jctc.1c00950
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发表时间:
2021
期刊:
The Journal of Chemical Theory and Computation
影响因子:
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通讯作者:
Hiroki Uratani and Hiromi Nakai
Hiroki Uratani and Hiromi Nakai
中科院分区:
--
文献类型:
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作者:
SUEYOSHI Kazumasa;KITAMURA Manami;LEI Xinglin;KATAYAMA Ikuo;末吉和公・北村真奈美・雷興林・片山郁夫;Hiroki Uratani and Hiromi Nakai;Hiroki Uratani and Hiromi Nakai

文献摘要

相似文献

为了探索高复杂性化学系统中激发态动力学背后的科学,一种可扩展的非绝热分子动力学(MD)技术是必不可少的。在本研究中,通过在密度-功能紧密结合水平上处理电子自由度,我们开发并实现了一种减少缩放和多节点并行化的Ehrenfest MD方法。为了实现这一目标,我们引入了一个称为拼凑近似(PA)的概念,其中电子密度矩阵实时传播的有效哈密顿量被划分为一组局部部分。对于包含多达6000个原子的巨型二十面体富勒烯的数值结果表明,目前基于pa的方法的标度小于二次,在计算时间方面比传统的三次标度方法具有显着优势。对多节点并行化的加速性能进行了评价。此外,即使在电子激励空间离域的情况下,由外电场扰动引起的电子和结构动力学也可以用PA精确地再现。
To explore the science behind excited-state dynamics in high-complexity chemical systems, a scalable nonadiabatic molecular dynamics (MD) technique is indispensable. In this study, by treating the electronic degrees of freedom at the density-functional tight-binding level, we developed and implemented a reduced scaling and multinode-parallelizable Ehrenfest MD method. To achieve this goal, we introduced a concept called patchwork approximation (PA), where the effective Hamiltonian for real-time propagation of the electronic density matrix is partitioned into a set of local parts. Numerical results for giant icosahedral fullerenes, which comprise up to 6000 atoms, suggest that the scaling of the present PA-based method is less than quadratic, which yields a significant advantage over the conventional cubic scaling method in terms of computational time. The acceleration by the parallelization on multiple nodes was also assessed. Furthermore, the electronic and structural dynamics resulting from the perturbation by the external electric field were accurately reproduced with the PA, even when the electronic excitation was spatially delocalized.