Collaborative Research: NSCI: SI2-SSE: Time Stepping and Exchange-Correlation Modules for Massively Parallel Real-Time Time-Dependent DFT

合作研究：NSCI：SI2-SSE：大规模并行实时瞬态 DFT 的时间步进和交换相关模块

• 批准号: 1740204
• 负责人: Yosuke Kanai
• 金额: $ 25万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740204&HistoricalAwards=false
• 关键词: Collaborative; Research; NSCI; SI2; SSE

Recent advances in high-performance (HPC) computing allow simulations of quantum dynamics of electrons in complex materials, and such simulations are central to advancing various medical and semiconductor technologies, ranging from proton beam cancer therapy to fabricating faster and smaller electronics. At the same time, the increasing scale and complexity of modern high-performance computers exposed a need for development of scientific software that is tailored for computers with large numbers of processors so that simulations can efficiently take advantage of increasing computing power. This project advances scientific software for simulating quantum dynamics of electrons for high-performance computers with tens and hundreds of thousands of processors that are becoming widely available. This work builds the HPC academic research community around the proposed software by extending the existing software available for quantum dynamics simulation with better user-friendly features and analysis techniques. In the process, this project engages graduate students and early-career researchers to use and further develop scientific software for high-performance computers in general. Additionally, a summer school for hands-on training will be conducted. The open source software will be made available to the community on Github (public repository). Real-time propagation in time-dependent density functional theory (RT-TDDFT) is becoming increasingly popular for studying non-equilibrium electronic dynamics both in the linear regime and beyond linear response. RT-TDDFT can be combined to study coupled dynamics of quantum-mechanical electrons with the movement of classical ions within Ehrenfest dynamics. In spite of its great promise, RT-TDDFT is computationally very demanding, especially for studying large condensed-matter systems. The large cost arises from small time steps of numerical integration of the electron dynamics, rendering accurate (hybrid) exchange-correlation (XC) functionals unfeasible, despite their clear benefits. In addition, while modern high-performance computing (HPC) helps tackling great scientific questions, massively parallel, hybrid-paradigm architectures present new challenges. Theoretical and algorithmic methods need to be developed in order to take full advantage of modern massively parallel HPC. This work builds new modules for the RT-TDDFT software component of the Qb@ll code, that enables a large community of researchers to perform advanced first-principles simulations of non-equilibrium electron dynamics in complex condensed-phase systems, using massively parallel HPC. This is done through developing (1) new modules for numerical integration that propagate the underlying non-linear partial differential equations in real time with high efficiency and accuracy, and (2) new modules for improved approximations of the underlying electronic structure, using a modern meta-generalized-gradient XC functional. Furthermore, the work builds the HPC academic research community around RT-TDDFT within the Qb@ll code through (1) development of user-friendly features that interface Qb@ll with other code and analysis techniques and (2) engagement of early-career scientists by incorporating hands-on training on RT-TDDFT using the Qb@ll code in TDDFT summer school.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering, the Materials Research Division and Chemistry Division in the Directorate of Mathematical and Physical Sciences.

高性能 (HPC) 计算的最新进展允许模拟复杂材料中电子的量子动力学，这种模拟对于推进各种医疗和半导体技术（从质子束癌症治疗到制造更快、更小的电子产品）至关重要。与此同时，现代高性能计算机的规模和复杂性不断增加，需要开发专为具有大量处理器的计算机定制的科学软件，以便模拟可以有效地利用不断增长的计算能力。该项目开发了用于模拟电子量子动力学的科学软件，用于高性能计算机，该计算机具有数以万计的处理器，并且正在广泛使用。这项工作通过使用更好的用户友好功能和分析技术扩展可用于量子动力学模拟的现有软件，围绕所提出的软件建立了 HPC 学术研究社区。在此过程中，该项目吸引研究生和早期职业研究人员使用和进一步开发用于高性能计算机的科学软件。此外，还将举办暑期实践培训学校。该开源软件将在 Github（公共存储库）上向社区提供。瞬态密度泛函理论 (RT-TDDFT) 中的实时传播在研究线性状态和线性响应之外的非平衡电子动力学方面变得越来越流行。 RT-TDDFT 可以结合起来研究量子力学电子的耦合动力学与 Ehrenfest 动力学中经典离子的运动。尽管 RT-TDDFT 前景广阔，但它对计算的要求非常高，特别是对于研究大型凝聚态系统。电子动力学数值积分的小时间步长产生了巨大的成本，使得精确的（混合）交换相关（XC）泛函不可行，尽管它们有明显的好处。此外，虽然现代高性能计算 (HPC) 有助于解决重大科学问题，但大规模并行、混合范式架构也带来了新的挑战。为了充分利用现代大规模并行 HPC，需要开发理论和算法方法。这项工作为 Qb@ll 代码的 RT-TDDFT 软件组件构建了新模块，使大量研究人员能够使用大规模并行 HPC 对复杂凝聚相系统中的非平衡电子动力学进行先进的第一原理模拟。这是通过开发（1）用于数值积分的新模块来实现的，该模块可以高效率和高准确度地实时传播底层非线性偏微分方程，以及（2）用于改进底层电子结构近似的新模块，使用现代元广义梯度 XC 泛函。此外，这项工作通过 (1) 开发将 Qb@ll 与其他代码和分析技术连接的用户友好功能，以及 (2) 早期职业生涯的参与，围绕 Qb@ll 代码中的 RT-TDDFT 构建 HPC 学术研究社区科学家们通过在 TDDFT 暑期学校中使用 Qb@ll 代码进行 RT-TDDFT 实践培训。该项目得到了计算机和信息科学与工程理事会高级网络基础设施办公室、材料研究司和化学司的支持在数学和物理科学理事会。

项目成果

Propagation of maximally localized Wannier functions in real-time TDDFT

实时 TDDFT 中最大局部 Wannier 函数的传播

• DOI: 10.1063/1.5095631
• 发表时间: 2019-05
• 期刊: The Journal of Chemical Physics
• 作者: Yost, Dillon C.;Yao, Yi;Kanai, Yosuke
• 通讯作者: Kanai, Yosuke

First-Principles Modeling of Electronic Stopping in Complex Matter under Ion Irradiation

离子辐照下复杂物质电子停止的第一性原理建模

• DOI: 10.1021/acs.jpclett.9b02975
• 发表时间: 2019-12
• 期刊: The Journal of Physical Chemistry Letters
• 作者: Yost, Dillon C.;Yao, Yi;Kanai, Yosuke
• 通讯作者: Kanai, Yosuke

Simulating electronic excitation and dynamics with real-time propagation approach to TDDFT within plane-wave pseudopotential formulation

使用平面波赝势公式中的 TDDFT 实时传播方法模拟电子激励和动力学

• DOI: 10.1063/5.0057587
• 发表时间: 2021-09
• 期刊: The Journal of Chemical Physics
• 作者: Shepard, Christopher;Zhou, Ruiyi;Yost, Dillon C.;Yao, Yi;Kanai, Yosuke
• 通讯作者: Kanai, Yosuke

Nuclear Quantum Effect and Its Temperature Dependence in Liquid Water from Random Phase Approximation via Artificial Neural Network

通过人工神经网络随机相位近似研究液态水中的核量子效应及其温度依赖性

• DOI: 10.1021/acs.jpclett.1c01566
• 发表时间: 2021-07
• 期刊: The Journal of Physical Chemistry Letters
• 作者: Yao, Yi;Kanai, Yosuke
• 通讯作者: Kanai, Yosuke

First-Principles Demonstration of Nonadiabatic Thouless Pumping of Electrons in a Molecular System

分子系统中电子非绝热无源泵浦的第一性原理论证

• DOI: 10.1021/acs.jpclett.1c01037
• 发表时间: 2021-05
• 期刊: The Journal of Physical Chemistry Letters
• 作者: Zhou, Ruiyi;Yost, Dillon C.;Kanai, Yosuke
• 通讯作者: Kanai, Yosuke