相关实验视频

Updated: Jul 19, 2025

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

通过张量超收缩来更快更准确地交换固体

Adam Rettig^1,2, Joonho Lee³, Martin Head-Gordon^1,2

¹Department of Chemistry, University of California, Berkeley, California 94720, United States.

Journal of chemical theory and computation

|August 16, 2023

概括

我们开发了一种使用张量超收缩 (THC) 的新方法,使混合密度函数理论 (DFT) 的计算在大型系统中计算可行. 这种方法显著降低了计算成本和内存使用,用于准确的电子结构计算.

科学领域:

计算化学的计算化学
材料科学材料科学材料科学
量子力学就是量子力学.

背景情况:

混合密度函数理论 (DFT) 对于大型周期系统来说,由于精确交换计算的成本,因此在计算上昂贵.
开发高效的方法对于将精确的量子力学模拟应用于更大,更复杂的材料至关重要.

研究的目的:

为了减少定期混合DFT计算的计算扩展.
通过降低计算成本和内存要求,使更大的系统能够进行准确的电子结构计算.

主要方法:

张量超收缩 (THC) 近似的应用,也称为互极分离密度拟合,用于周期混合 DFT.
使用高斯平面波的方法与高斯型轨道.
开发一种算法,通过THC (THC-oo-K) 通过THC (THC-oo-K) 仅适应被占用的轨道产品,以进一步节省计算成本.

主要成果:

提议的THC方法降低了计算缩放的基础函数和k点的数量.
THC-oo-K 算法以 k 点进行线性缩放,以单元细胞大小进行立方缩放,并且没有明确的依赖基础集大小.
观察到显著的加速度和减少内存使用,特别是在较大的k点网格中.
实现了对自我一致的计算的充分准确性,正如在晶体上的混合DFT计算所证明的那样.

结论:

张量超收缩 (THC) 为周期混合DFT计算提供了一种高效和准确的方法.
THC-oo-K 算法有效降低了计算成本和内存,使更大的系统更容易处理.
这种方法对推进计算材料科学和凝聚物质物理学研究具有前途.

更多相关视频

Analyzing Protein Dynamics Using Hydrogen Exchange Mass Spectrometry

Analyzing Protein Dynamics Using Hydrogen Exchange Mass Spectrometry

Published on: November 29, 2013

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

Published on: August 2, 2012

相关概念视频

Convolution Properties I

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

Fast Fourier Transform

Fast Fourier Transform

The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...

Ion Exchange

Ion Exchange

Ion exchange chromatography separates charged molecules from a solution by reversibly exchanging them with mobile, or 'active', ions associated with the oppositely charged stationary phase. This method can be used to separate ions, soften and deionize water, and purify solutions. The polymers comprising the ion-exchange column are high-molecular-weight and chemically stable polymers, crosslinked to be porous and essentially insoluble. They are also functionalized with either acidic or...

Inertia Tensor

Inertia Tensor

The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...

Scalar and Vector Triple Products

Scalar and Vector Triple Products

Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...