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相关概念视频

¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.0K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.0K
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

1.7K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
1.7K
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

1.3K
A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
1.3K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.5K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.5K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K

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通过物理引导的机器学习来解决光谱数据的反转问题.

Hwiwoo Park1, Jun H Park2, Jungseek Hwang3

  • 1Department of Physics, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea.

Scientific reports
|April 19, 2024
PubMed
概括

我们开发了一种新的机器学习方法,即规范反复推断机 (rRIM),以从光学光谱中提取配对接函数. 这种方法提高了准确性,并减少了对复杂科学问题的数据需求.

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科学领域:

  • 计算物理 计算物理
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 导出配对合剂的功能对于理解材料特性至关重要.
  • 实验性光学光谱对准确的数据分析提出了挑战.
  • 反向问题,像这个问题一样,往往是错误的,需要强大的方法.

研究的目的:

  • 引入一种新的机器学习方法,即正规化的反复推理机器 (rRIM).
  • 为了应对从光学光谱中推导配对合合功能的挑战.
  • 开发一种对噪声稳固且对数据变化灵活的方法.

主要方法:

  • 规范的反复推理机器 (rRIM) 是作为机器学习模型开发的.
  • 物理原理被整合到rRIM的培训和推理阶段.
  • 通过测量光学光谱数据,rRIM进行了训练和测试.

主要成果:

  • rRIM成功地从实验光学光谱中获得了可靠的配对接功能.
  • 该方法在光谱数据中证明了对噪声的稳定性.
  • rRIM在处理销售数据方面表现出灵活性,数据要求降低.

结论:

  • 正规化的反复推断机器 (rRIM) 为分析光谱提供了一个强大的新工具.
  • 这种方法为涉及第一种弗雷德霍尔姆积分方程的反向问题提供了有希望的解决方案.
  • 由于rRIM能够结合物理原理,因此它在科学发现中的应用性得到了提高.