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Interference and Diffraction02:18

Interference and Diffraction

Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
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X-ray Crystallography02:18

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Diffraction
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Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
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Related Experiment Video

Updated: Jun 23, 2026

Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
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Published on: September 26, 2014

TorchDisorder: A Differentiable Framework for Generating Physically Realistic Disorder Structures from Experimental

Advait Gore1, Xander Gouws1, Conrard Giresse Tetsassi Feugmo1,2,3

  • 1Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.

Journal of Chemical Theory and Computation
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

TorchDisorder, a new PyTorch framework, uses gradient-based optimization to determine amorphous material structures. It significantly speeds up the process compared to traditional methods, improving computational efficiency and accuracy for disordered systems.

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Last Updated: Jun 23, 2026

Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
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Area of Science:

  • Condensed-matter physics
  • Materials science
  • Computational materials science

Background:

  • Determining atomic structures of amorphous materials is challenging due to lack of long-range periodicity.
  • Conventional diffraction analysis and stochastic reverse Monte Carlo (RMC) methods have limitations in efficiency and enforcing chemical realism.

Purpose of the Study:

  • Introduce TorchDisorder, a PyTorch-based framework for efficient and accurate atomic structure determination of amorphous materials.
  • Replace stochastic sampling in RMC with gradient-based optimization for improved computational performance and constraint satisfaction.

Main Methods:

  • Developed a PyTorch framework integrating GPU-accelerated neighbor lists (torch-sim), augmented Lagrangian optimization (Cooper), and a differentiable structure factor engine.
  • Utilized automatic differentiation to propagate gradients through the Faber-Ziman transform for structure determination.
  • Implemented coordination constraints (tetrahedral, octahedral) via JSON configuration files derived from crystalline precursors.

Main Results:

  • Achieved quantitative agreement with experimental scattering data for silica, germania, and lithium thiophosphate glasses (R² ≥ 0.955).
  • Obtained structural models within 5000 gradient steps using a single diffraction dataset, demonstrating superior convergence speed over stochastic RMC.
  • Successfully enforced chemically realistic local environments and coordination constraints.

Conclusions:

  • TorchDisorder offers a computationally efficient and accurate alternative to traditional methods for atomic structure determination in amorphous materials.
  • The framework's gradient-based approach and constraint satisfaction capabilities advance the study of disordered materials relevant to energy technology.