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相关概念视频

Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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What Are Outliers?01:12

What Are Outliers?

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Potential-Energy Criterion for Equilibrium01:16

Potential-Energy Criterion for Equilibrium

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Potential energy or potential function plays an essential role in determining the stability of a mechanical system. If a system is subjected to both gravitational and elastic forces, the potential function of the system can be expressed as the algebraic sum of gravitational and elastic potential energy. If the system is in equilibrium and is displaced by a small amount, then the work done on the system equals the negative of the change in the system's potential energy from the initial to...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Potential Energy00:52

Potential Energy

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The energy stored by a structure and location of matter in space is called potential energy. For instance, raising a kettlebell changes its spatial location and increases its potential energy. Similarly, a stretched rubber band contains potential energy which, under certain conditions, can be converted into other forms of energy, such as kinetic energy.
Chemical bonds that form attractive forces between atoms also contain potential energy, called chemical energy. When a chemical reaction...
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相关实验视频

Updated: May 27, 2025

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
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对于反应性机器学习的潜在能量表面的异常值检测.

Luis Itza Vazquez-Salazar1, Silvan Käser1, Markus Meuwly1

  • 1Department of Chemistry, University of Basel, Basel, Switzerland.

npj computational materials
|February 18, 2025
PubMed
概括

测试了不确定性量化 (UQ) 方法,以识别分子潜在能量表面的错误. 合并模型在异常值检测方面被证明是最有效的,其表现优于高斯混合模型和深度证据回归.

科学领域:

  • 计算化学是一种计算化学.
  • 化学物理 化学物理
  • 机器学习在科学中的应用.

背景情况:

  • 准确的潜在能量表面 (PES) 对于模拟化学反应至关重要.
  • 在 PES 中识别不可靠的预测对于高效的模型改进至关重要.
  • 不确定性量化 (UQ) 为评估预测信心提供了一个框架.

研究的目的:

  • 评估不同的UQ方法来检测反应分子PES中的异常值.
  • 为了比较UQ的合集,深度证据回归 (DER) 和高斯混合模型 (GMM) 的性能.
  • 确定提高 PES 开发准确性和效率的策略.

主要方法:

  • 应用三个UQ方法:合奏,DER和GMM.
  • 测试syn-Criegee和乙烯基氧化之间的H转移反应.
  • 基于预测不确定性的异常结果检测性能分析.

主要成果:

  • 集成模型在检测异常值方面表现出最高的准确性 (前25个结构的~90%).
  • GMM表现中等 (在前1000个结构中约为50%).
  • 德尔的业绩受到其统计假设的显著限制.
  • 确定了与平均误差相关的基于结构的指标.
关键词:
物理化学 物理化学理论化学是一种理论化学.

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结论:

  • 基于集体的UQ是一种强大的方法,用于识别PES计算中的不可靠数据点.
  • 在这种情况下,GMM为UQ提供了一个可行的替代方案.
  • 识别的基于结构的指标可以加快对PES的神经网络模型的改进.