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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
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In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
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In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
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深度学习密度功能性扰乱理论

He Li1,2, Zechen Tang1, Jingheng Fu1

  • 1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China.

Physical review letters
|March 15, 2024
PubMed
概括
此摘要是机器生成的。

神经网络加速密度函数干扰理论 (DFPT) 对材料科学的计算. 这种方法显著提高了计算效率和预测材料性能的准确性.

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 量子化学 是一个量子化学.
  • 科学中的人工智能.

背景情况:

  • 第一原理计算对于理解材料属性至关重要,但在计算上昂贵.
  • 密度功能扰动理论 (DFPT) 能够计算材料响应特性,将理论与实验联系起来.
  • 目前的DFPT方法面临着显著的计算瓶,限制了它们的广泛应用.

研究的目的:

  • 开发一个使用神经网络执行DFPT计算的一般框架.
  • 显著提高DFPT的计算效率.
  • 为了证明提出的神经网络基础的方法的准确性和效率.

主要方法:

  • 实施使用神经网络进行DFPT计算的一般框架.
  • 在神经网络中应用自动差异化,用于准确的衍生计算.
  • 通过研究电子 - 声波合和相关材料特性进行验证.

主要成果:

  • 在DFPT计算的计算效率方面取得了实质性的改进.
  • 证明神经网络方法在预测材料特性方面具有很好的准确性.
  • 成功地统一了深度学习密度函数理论与DFPT.

结论:

  • 拟议的神经网络框架为DFPT计算提供了一个高效和准确的替代方案.
  • 这项工作弥合了人工智能和材料科学中的初学者方法之间的差距.
  • 在第一原则计算中开辟了开发人工智能的新途径.