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Related Concept Videos

Determination of Crystal Structures01:29

Determination of Crystal Structures

In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...
X-ray Crystallography02:18

X-ray Crystallography

The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
Symmetry Elements in a Crystal01:27

Symmetry Elements in a Crystal

Crystal symmetry operations are isometric transformations that map objects onto indistinguishable copies while preserving distances, angles, and volumes. The simplest symmetry operation is translation, which shifts the entire infinite crystal lattice parallelly by a translation vector.Crystallographic rotations involve rotations by an angle of 2π/n around an axis without changing the positions of points on the axis. It is called the rotational axis of the symmetry, denoted by n. The combination...
Crystallographic Point Groups01:29

Crystallographic Point Groups

Crystallographic point groups represent the various symmetry operations that can occur within crystals. They are unique in that at least one point will always remain unchanged during these actions. For instance, consider the triclinic system. This system, devoid of any axis or plane of symmetry, aligns with the C1 and Ci point groups.where Cᵢ is characterized solely by a center of inversion.Contrastingly, the monoclinic system introduces an element of symmetry. This system with one plane and...
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...

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  1. Home
  3. Siamese Foundation Models For Crystal Structure Prediction.
  1. Home
  3. Siamese Foundation Models For Crystal Structure Prediction.

Related Experiment Video

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae
09:15

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

Siamese foundation models for crystal structure prediction.

Liming Wu1,2,3, Wenbing Huang4,5,6, Rui Jiao7,8

  • 1Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China.

Nature Communications
|May 6, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed Diffusion-based crystAl Omni (DAO), a new AI framework for predicting crystal structures. DAO significantly accelerates materials discovery by accurately generating complex structures faster than traditional methods.

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Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source
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Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source

Published on: April 23, 2021

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Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae
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Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
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Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source
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Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source

Published on: April 23, 2021

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence in Materials Discovery

Background:

  • Predicting crystal structures from chemical composition is crucial for materials discovery but challenging due to complex 3D geometries.
  • Existing computational methods can be slow and struggle with complex or novel structures.

Purpose of the Study:

  • To introduce Diffusion-based crystAl Omni (DAO), a novel pretrain-finetune framework for enhanced crystal structure prediction.
  • To leverage Siamese foundation models for structure generation and energy prediction to improve accuracy and speed.

Main Methods:

  • Developed DAO, integrating a structure generator and an energy predictor within a Siamese network framework.
  • Pretrained the generator using a two-stage pipeline on a large dataset, utilizing the predictor for configuration relaxation and guided sampling.
  • Validated the framework on established benchmarks and real-world superconductor materials.

    • Main Results:

    • Pretraining significantly improved performance across various backbone architectures on standard benchmarks.
    • Synergistic interaction between the generator and predictor mutually enhanced both model components.
    • Achieved a 100% match rate and low atomic-position error for Cr6Os2, outperforming DFT-based methods by over 2000x per iteration.

    Conclusions:

    • DAO demonstrates superior performance and efficiency in crystal structure prediction compared to conventional computational approaches.
    • The framework shows significant potential for accelerating the discovery of novel materials, including complex superconductors.
    • The integrated generator-predictor approach offers a powerful new paradigm for computational materials science.