Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

4.7K
Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
4.7K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

30.5K
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...
30.5K
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

48.0K
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,...
48.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

PhAI: A deep-learning approach to solve the crystallographic phase problem.

Science (New York, N.Y.)·2024
Same author

Attraction between Like Charged Ions in Ionic Liquids: Unveiling the Enigma of Tetracyanoborate Anions.

The journal of physical chemistry letters·2024
Same author

Orthorhombic charge density wave on the tetragonal lattice of EuAl<sub>4</sub>.

IUCrJ·2022
Same author

Single-crystal-to-single-crystal phase transitions of commensurately modulated sodium saccharinate 1.875-hydrate.

IUCrJ·2021
Same author

On the puzzling case of sodium saccharinate 1.875-hydrate: structure description in (3+1)-dimensional superspace.

Acta crystallographica Section B, Structural science, crystal engineering and materials·2020
Same journal

Case study of using the single-atom R1 method to solve a small protein structure.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

Beyond complementarity: a reverse-engineering framework for de novo crystal structure determination from EXAFS.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

Crystallography in Open Science and its open educational resources.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

From atoms to a data bank: optimizing transferability of electron-density symmetry.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

Twenty-Sixth General Assembly and International Congress of Crystallography, Melbourne, Australia, 22-29 August 2023.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

MIDAS: a quantitative framework for high-energy diffraction microscopy. Part II: accuracy, robustness and best practices.

Acta crystallographica. Section A, Foundations and advances·2026
See all related articles
  1. Home
  3. On Artificial Crystal Structure Generation For Solving The Phase Problem With Deep Learning.
  1. Home
  3. On Artificial Crystal Structure Generation For Solving The Phase Problem With Deep Learning.

Related Experiment Video

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K

On artificial crystal structure generation for solving the phase problem with deep learning.

Džonatans Miks Melgalvis1, Toms Rekis2

  • 1Faculty of Medicine and Life Sciences, University of Latvia, Jelgavas iela 1, Riga LV1004, Latvia.

Acta Crystallographica. Section A, Foundations and Advances
|November 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial crystal structures aid neural networks in solving the crystallographic phase problem. Retraining the PhAI network on new artificial data significantly improves its ability to analyze larger unit-cell structures.

Keywords:
artificial crystal structuresdeep learningphase problem

More Related Videos

Optimization of Crystal Growth for Neutron Macromolecular Crystallography
12:29

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

5.9K
Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography
18:45

Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography

Published on: September 2, 2012

25.6K

Related Experiment Videos

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K
Optimization of Crystal Growth for Neutron Macromolecular Crystallography
12:29

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

5.9K
Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography
18:45

Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography

Published on: September 2, 2012

25.6K

Area of Science:

  • Crystallography
  • Materials Science
  • Artificial Intelligence

Background:

  • Solving the crystallographic phase problem is crucial for determining crystal structures.
  • Neural networks offer a promising approach for phase retrieval.
  • Generating realistic artificial crystal structures is essential for training these networks.

Purpose of the Study:

  • To present and discuss methods for generating artificial crystal structures for neural network training.
  • To evaluate the performance of the PhAI neural network on experimental data after retraining with novel artificial datasets.

Main Methods:

  • Structure generation involves sampling unit-cell parameters and atom placement.
  • Lattice basis vectors are generated from sampled unit-cell volumes.
  • Molecule-like fragments are generated using database data to guide atom placement, complementing random methods.

    • Main Results:

    • The PhAI neural network was benchmarked and retrained using various artificial datasets.
    • Retraining PhAI with a new type of artificial data demonstrated significant improvements.
    • The improved model showed enhanced generalization for solving the phase problem in larger unit-cell structures.

    Conclusions:

    • Artificial data generation is a viable strategy for enhancing neural network performance in crystallography.
    • The developed methods enable scalable generation of crystal structures for training.
    • The study highlights the potential of AI in advancing crystallographic structure determination.