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相关概念视频

Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Machines: Problem Solving I01:22

Machines: Problem Solving I

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.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Machines: Problem Solving II01:30

Machines: Problem Solving II

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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A Guided Materials Screening Approach for Developing Quantitative Sol-gel Derived Protein Microarrays
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一种用于大规模选材料的通用机器学习算法

George S Fanourgakis1, Konstantinos Gkagkas2, Emmanuel Tylianakis3

  • 1Department of Chemistry , University of Crete , Voutes Campus , GR-70013 Heraklion , Crete , Greece.

Journal of the American Chemical Society
|February 5, 2020
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 模型通过使用原子类型作为描述符更准确地预测金属有机框架 (MOF) 中的气体吸附. 这种方法需要较少的培训数据,并且对新材料更普遍.

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科学领域:

  • 材料科学
  • 计算化学
  • 机器学习

背景情况:

  • 机器学习 (ML) 为预测金属有机框架 (MOF) 等纳米材料中的气体吸附提供了一个计算高效的替代方案.
  • 之前的机器学习模型依赖于结构构件,这可能会限制通用性,并需要大量的训练数据.

研究的目的:

  • 提高ML模型的准确性和普遍性,以预测MOF中的气体吸附能力.
  • 通过使用原子类型而不是构建块来引入化学直觉到ML描述器中.

主要方法:

  • 使用随机森林算法预测数千个假设的MOF的甲和二氧化碳吸附能力.
  • 开发了基于"原子类型"的新描述符,以捕捉MOF的化学特性.
  • 在各种热力学条件下评估模型性能.

主要成果:

  • 使用原子类型的机器学习预测在精度上明显优于基于构建块的模型.
  • 培训所需的MOF数量减少了一倍.
  • 通过成功预测不同类型材料的吸附性质,证明了通用性和可转移性.

结论:

  • 将原子类型作为描述符增加了准确性,并减少了在MOF中基于ML的气体吸附预测的数据要求.
  • 拟议的原子类型描述器方法提供了更大的通用性和可转移性,使不同材料家族的预测成为可能.
  • 这种方法代表了计算选和设计用于气体吸附应用的新材料的重大进步.