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Related Concept Videos

Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
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¹H NMR: Complex Splitting01:13

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A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
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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.
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IR Spectroscopy: Molecular Vibration Overview01:24

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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
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UV–Vis Spectroscopy: Molecular Electronic Transitions01:16

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In Ultraviolet–Visible (UV–Vis) spectroscopy, the absorption of electromagnetic radiation is used to probe the electronic structure of molecules. This technique provides insights into molecular electronic transitions, particularly the movement of electrons between different molecular orbitals. Radiation is absorbed if the energy of the electromagnetic radiation passing through the molecule is precisely equal to the energy difference between the excited and ground states. During this...
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A machine learning route between band mapping and band structure.

R Patrick Xian1,2, Vincent Stimper3, Marios Zacharias4,5

  • 1Fritz Haber Institute of the Max Planck Society, Berlin, Germany. xrpatrick@gmail.com.

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Summary

This study introduces a machine learning pipeline to reconstruct electronic band structures from photoemission data. This method enhances the analysis of solid-state materials and enables new insights into their properties.

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Area of Science:

  • Solid-state physics
  • Materials science
  • Computational materials science

Background:

  • Electronic band structure and crystal structure are key identifiers for solid-state materials.
  • Current computational methods limit the extraction of quasiparticle dispersion from photoemission band mapping data.
  • Databases of crystal structures are extensive, but band structure data extraction is challenging.

Purpose of the Study:

  • To develop a computational pipeline for accurate band-structure reconstruction from photoemission data.
  • To overcome limitations in current methods for analyzing large-scale photoemission datasets.
  • To integrate machine learning with theoretical calculations for materials science.

Main Methods:

  • Developed a pipeline combining probabilistic machine learning with data processing, optimization, and evaluation.
  • Leveraged theoretical calculations to aid in band-structure reconstruction.
  • Applied the pipeline to reconstruct all 14 valence bands of a semiconductor.

Main Results:

  • The pipeline demonstrated excellent performance on benchmark and other materials datasets.
  • Successfully reconstructed electronic band structures, revealing previously inaccessible momentum-space information.
  • Showcased the ability to analyze both global and local structural information.

Conclusions:

  • The developed pipeline offers a scalable approach for feature extraction in multidimensional data.
  • Combines machine learning with domain knowledge for advanced materials analysis.
  • Paves the way for integrating advanced band structure analysis with materials science databases.