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相关概念视频

Chemical Equilibria: Systematic Approach to Equilibrium Calculations01:21

Chemical Equilibria: Systematic Approach to Equilibrium Calculations

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Equilibrium calculations for systems involving multiple equilibria are often complex. For example, to calculate the solubility of a sparingly soluble salt in an aqueous solution in the presence of a common ion, one must consider all the equilibria in this solution. Calculations for these systems can be complicated and tedious, so a systematic approach with a series of steps is often helpful. The process is detailed below.
The first step is to identify all the chemical reactions involved, The...
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Applications of Integration to Probability Density Functions01:27

Applications of Integration to Probability Density Functions

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Continuous probability distributions are used to model random variables that can take on any real value within a specified range. These variables do not take on isolated or countable values but rather exist on a continuum. For example, the height of an individual can be measured with increasing precision—such as 163.5 or 165.25 centimeters—demonstrating that height is a continuous random variable.The behavior of such variables is described using a probability density function (PDF),...
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Calculating the Equilibrium Constant02:46

Calculating the Equilibrium Constant

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The equilibrium constant for a reaction is calculated from the equilibrium concentrations (or pressures) of its reactants and products. If these concentrations are known, the calculation simply involves their substitution into the Kc expression.
For example, gaseous nitrogen dioxide forms dinitrogen tetroxide according to this equation:
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Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

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The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
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Calculating pH Changes in a Buffer Solution02:45

Calculating pH Changes in a Buffer Solution

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A buffer can prevent a sudden drop or increase in the pH of a solution after the addition of a strong acid or base up to its buffering capacity; however, such addition of a strong acid or base does result in the slight pH change of the solution. The small pH change can be calculated by determining the resulting change in the concentration of buffer components, i.e., a weak acid and its conjugate base or vice versa. The concentrations obtained using these stoichiometric calculations can be used...
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Numerical Calculations01:24

Numerical Calculations

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In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
The solution to a problem is obtained using different methods. While manually solving algebraic symbols is one of the most common methods, the graphical method is often preferred. Computers...
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Precise Cellular Ablation Approach for Modeling Acute Kidney Injury in Developing Zebrafish
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一个混合精度的方法,预先条件的egegensolver有效的密度函数计算在AI专注的GPU上.

Jeheon Woo1, Sunghwan Choi2

  • 1Department of Supercomputing Acceleration Research, Division of National Supercomputing, Korea Institute of Science and Technology Information, Daejeon 34141, Republic of Korea.

Journal of chemical theory and computation
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概括

本研究引入了密度函数理论 (DFT) 计算的混合精度方法,使得以人工智能为中心的GPU上的模拟速度更快. 该方法保持了准确性,同时显著提高了计算速度和系统大小容量.

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科学领域:

  • 计算化学计算化学
  • 材料科学 材料科学 材料科学
  • 高性能计算 高性能计算

背景情况:

  • 图形处理单元 (GPU) 具有先进的计算化学.
  • 专注于人工智能的GPU在双精度 (FP64) 量子化学操作方面效率低下.
  • 有限的GPU内存需要对密度函数理论 (DFT) 进行算法适应.

研究的目的:

  • 开发一种混合精度策略,用于实空间 DFT 中的代矩阵对角化.
  • 为了保持数值准确性,同时提高GPU上的计算效率.
  • 为了实现更大规模的电子结构模拟和扩大GPU的适用性.

主要方法:

  • 实施了用于矩阵对角化的系统混合精度策略.
  • 使用单精度 (FP32) 和大脑浮点 (BF16) 数学.
  • 在各种材料系统中开发并验证了一种混合精度的自身溶解器.

主要成果:

  • 与FP64.4相比,在对角化方面实现了高达10倍的加速.
  • 将可行的系统大小扩大了约50%.
  • 展示了以人工智能为重点的GPU与以HPC为重点的GPU的可比性能.

结论:

  • 混合精度策略保留了DFT计算中的数值准确性.
  • 专注于人工智能的GPU可以有效地用于大规模的电子结构模拟.
  • 开发的方法提高了对先进的计算化学工具的可访问性.