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Acceleration and Verification of Large-Scale First-Principles Molecular Dynamics.

机译:大规模第一性原理分子动力学的加速和验证。

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摘要

Kohn-Sham density functional theory (DFT) is an important computational tool in materials science for studying molecules, metals, insulators, and more. However, using DFT to study even modest size systems and timescales is extremely computationally demanding. To enable calculations on larger systems and longer timescales, much research has been done on reduced scaling methods based on localized orbitals. One such reduced scaling method called the Recursive Subspace Bisection (RSB) method was recently proposed to reduce the cost of hybrid density functional theory calculations. However, its applicability was limited to the case of computing hybrid functionals and to the study of relatively homogeneous systems. In this work, we expand and refine the RSB method so that it may be applied to a wider class of applications. First, we develop scheduling algorithms to allow for strong scaling parallel performance when applied to inhomogeneous systems. Second, we perform a detailed analysis of the error introduced by the RSB method, providing important reference data for future calculations on new systems. Finally, we expand the use of the RSB method beyond accelerating hybrid functionals to compressing wavefunction data. Throughout this work, we will also use the RSB method to provide new insight into the localization properties for a variety of systems. Together, the improvements and insights in this work will enable a new class of once intractable large-scale DFT calculations.
机译:Kohn-Sham密度泛函理论(DFT)是材料科学中用于研究分子,金属,绝缘体等的重要计算工具。但是,使用DFT甚至研究中等大小的系统和时间尺度,对计算的要求都很高。为了能够在更大的系统和更长的时间尺度上进行计算,已经对基于局部轨道的缩减尺度方法进行了大量研究。最近提出了一种称为递归子空间对分(RSB)方法的缩减比例缩放方法,以降低混合密度泛函理论计算的成本。但是,其适用性仅限于计算混合功能的情况以及相对同类系统的研究。在这项工作中,我们扩展和完善了RSB方法,以便可以将其应用于更广泛的应用程序类别。首先,我们开发了调度算法,当应用于非均质系统时,可以实现强大的扩展并行性能。其次,我们对RSB方法引入的误差进行详细分析,为将来在新系统上的计算提供重要的参考数据。最后,我们将RSB方法的使用范围扩展到了加速混合功能之外,从而进一步压缩了波函数数据。在整个工作中,我们还将使用RSB方法提供对各种系统的本地化属性的新见解。总之,这项工作中的改进和见识将使一类曾经难以处理的大规模DFT计算成为可能。

著录项

  • 作者单位

    University of California, Davis.;

  • 授予单位 University of California, Davis.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 90 p.
  • 总页数 90
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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