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相关概念视频

Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
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The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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几何优化:不同开源几何优化器的比较

Akhil Shajan1, Madushanka Manathunga1,2, Andreas W Götz3

  • 1Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.

Journal of chemical theory and computation
|October 23, 2023
PubMed
概括
此摘要是机器生成的。

与笛卡尔坐标相比,内部坐标加速了分子几何优化. 机器学习和特定的算法,如ASE/Berny和ASE/Sella显示出对高效的能源最小化的承诺.

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科学领域:

  • 计算化学的计算化学
  • 分子建模分子建模
  • 量子化学 是一个量子化学.

背景情况:

  • 准确的分子几何学对于理解化学性质和反应至关重要.
  • 有效的几何优化算法对于计算化学至关重要.
  • 评估开源工具对于可访问性和可重复性至关重要.

研究的目的:

  • 为了比较各种与量子交互计算内核 (QUICK) 接口的开源几何优化代码的性能.
  • 评估坐标系 (内部与笛卡尔) 对优化效率的影响.
  • 为了评估一个机器学习方法 (高斯过程回归) 的能量最小化.

主要方法:

  • 在30个有机分子的贝克测试集上进行的能量最小化.
  • 几何优化代码的比较,包括geomeTRIC,DL-FIND和ASE (使用伯尼和塞拉算法).
  • 使用 QUICK 进行梯度和能量计算,使用 Hartree-Fock 理论和 6-31G** 基准设置进行机器学习评估.

主要成果:

  • 内部坐标总是需要比笛卡尔坐标更少的优化步骤.
  • 选择最初的赫森和更新方法显著影响了趋同率.
  • 高斯过程回归 (GPR) 模型在内部坐标方面表现出卓越的性能.
  • 与其他测试方法相比,ASE/Berny和ASE/Sella显示出更快的趋同.

结论:

  • 建议使用内部坐标来更快地优化几何.
  • ASE/Berny和ASE/Sella适用于大规模的优化任务.
  • 与DL-FIND集成的QUICK提供了一个可行的单个可执行的优化解决方案.