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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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自动MatRegressor:解放机器学习的炼金术士

Yue Liu1, Shuangyan Wang2, Zhengwei Yang2

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; Shanghai Engineering Research Center of Intelligent Computing System, Shanghai 200444, China; Zhejiang Laboratory, Hangzhou 311100, China.

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概括
此摘要是机器生成的。

自动MatRegressor通过自动化机器学习 (ML) 模型创建来加速材料属性预测. 这种方法使用meta-learning来选择最佳的算法和超参数,降低实验成本并提高准确性.

关键词:
自动建模自动建模机器学习是机器学习.材料属性预测 材料属性预测超级学习 (Meta-learning) 是一种学习方式.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学
  • 数据科学数据科学数据科学

背景情况:

  • 机器学习 (ML) 对于通过识别模式和关系来预测材料特性至关重要.
  • 目前用于材料科学的ML模型开发受到耗时和劳动密集型实验数据采集的阻碍.
  • 高精度的ML模型需要在算法选择和超参数调整方面拥有广泛的数据和专业知识.

研究的目的:

  • 开发一种用于材料属性预测的自动机器学习 (ML) 建模方法.
  • 为了降低计算成本和实验力度,为材料科学建立准确的ML模型.
  • 介绍Auto-MatRegressor,这是一个基于meta-learning的方法,用于自动化算法选择和超参数优化.

主要方法:

  • 开发了Auto-MatRegressor,这是一个自动建模框架,利用对历史材料数据集的元学习.
  • 整合了27个表征数据集的元特征和18个常见材料科学算法的性能.
  • 实施了一个协作型的超级学习策略,增强了来自材料类别树的域名知识,用于算法推.

主要成果:

  • 与传统方法相比,Auto-MatRegressor证明了高效的算法选择和超参数优化.
  • 自动化方法显著降低了计算成本,同时在60个不同的数据集中实现了良好的预测准确性.
  • 该系统在加速用于材料属性预测的ML模型的构建方面表现出有效性.

结论:

  • 自动MatRegressor为材料发现和设计中的自动化ML模型开发提供了可扩展和高效的解决方案.
  • 超学习方法有效地利用过去的建模经验来指导新模型的构建.
  • 这种方法支持动态扩展,随着更多的数据和算法变得可用,可以进行持续改进.