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相关概念视频

Introduction to Structures01:30

Introduction to Structures

1.0K
A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
There are three main...
1.0K
Random Sampling Method01:09

Random Sampling Method

11.0K
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...
11.0K
Stratified Sampling Method01:16

Stratified Sampling Method

11.9K
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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.9K
Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
848
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.0K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.0K
Random Variables01:09

Random Variables

11.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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相关实验视频

Updated: Jun 12, 2025

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
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Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

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超越理论驱动的发现:引入热随机搜索和数据衍生结构.

Chris J Pickard1,2

  • 1Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, UK. cjp20@cam.ac.uk.

Faraday discussions
|September 19, 2024
PubMed
概括

新的机器学习方法通过增强随机结构搜索来加速材料发现. 这些方法,包括热AIRSS和EDDP,有效地找到复杂的低能结构,例如和碳元素.

科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 机器学习是机器学习.

背景情况:

  • 机器学习的原子间潜力 (MLIP) 显著加速了计算化学.
  • 传统的数据驱动方法与理论驱动的发现形成鲜明对比,例如ab initio随机结构搜索 (AIRSS).

研究的目的:

  • 引入结合ML加速与理论驱动结构搜索的新方法.
  • 提高发现低能耗材料配置的效率和范围.

主要方法:

  • 将短暂数据衍生潜力 (EDDP) 纳入AIRSS以进行偏向抽样.
  • 开发热AIRSS (热-AIRSS) 来处理更复杂的系统.
  • 基于参考结构和积极学习的EDDPs生成候选结构.

主要成果:

  • 在大型单元细胞中使用热AIRSS成功识别了复杂的结构.
  • 产生了多样化的低能碳结构,包括石墨,纳米管,富勒伦和四面体框架.
  • 使用ML加速搜索从相关的低能耗配置中恢复了pyrope石榴石结构.

结论:

  • 开发的方法提供了显著的加速,并使复杂的化学空间的探索.

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  • 这些ML增强技术为材料发现提供了强大的框架,补充了现有的生成模型.