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

5.7K
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...
5.7K
Determination of Crystal Structures01:29

Determination of Crystal Structures

44
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...
44
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

49.5K
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,...
49.5K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

31.6K
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...
31.6K
Unit Cells01:18

Unit Cells

50
A crystal's internal structure is an orderly array of atoms, ions, or molecules, and the details of this array significantly influence the solid's properties. In a crystal, periodically repeating 'structural motifs' - which could be atoms, molecules, or groups thereof - create a 'space lattice.' This is essentially a three-dimensional, infinite array of points, each surrounded by its neighbors in an identical way, forming the basic structure of the crystal.A 'unit cell' is a theoretical...
50
Crystallographic Point Groups01:29

Crystallographic Point Groups

42
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...
42

You might also read

Related Articles

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

Sort by
Same author

Tokenization for Molecular Foundation Models.

Journal of chemical information and modeling·2026
Same author

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.

Machine learning: science and technology·2025
Same author

Excess density as a descriptor for electrolyte solvent design.

The Journal of chemical physics·2025
Same author

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning.

Nature communications·2024
Same author

Electrowinning for Room-Temperature Ironmaking: Mapping the Electrochemical Aqueous Iron Interface.

The journal of physical chemistry. C, Nanomaterials and interfaces·2024
Same author

Modeling Scanning Electrochemical Cell Microscopy (SECCM) in Twisted Bilayer Graphene.

The journal of physical chemistry letters·2024
Same journal

Interplay between oxygen redox and interfacial stability of Li-rich positive electrodes in sulfide-based all-solid-state batteries.

Nature communications·2026
Same journal

Breaking dependence on melanisation imparts diversity to a dogmatic invasion strategy of phytopathogenic fungi.

Nature communications·2026
Same journal

Hydroxyl-rich nanocavities on perovskite enable nearly barrierless intramolecular hydrogen transfer for nitrate electroreduction to ammonia.

Nature communications·2026
Same journal

Household mobility responses to weather extremes in Kyrgyzstan.

Nature communications·2026
Same journal

Autonomous Motion Vision with Tri-bulk-heterojunctioned Organic Adaptation Transistor.

Nature communications·2026
Same journal

Tissue-adhesive hydrogel optical fiber for peripheral optogenetic neuromodulation.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

Microcrystallography of Protein Crystals and In Cellulo Diffraction
09:35

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

9.6K

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning.

Changwen Xu1, Shang Zhu1, Venkatasubramanian Viswanathan2,3

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, USA.

Nature Communications
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

We developed CLOUD, a machine learning model for predicting crystal properties. It uses a novel representation and integrates physics for accurate, scalable materials discovery.

More Related Videos

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
07:11

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

7.4K
Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals
07:57

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals

Published on: April 25, 2017

8.8K

Related Experiment Videos

Last Updated: Mar 19, 2026

Microcrystallography of Protein Crystals and In Cellulo Diffraction
09:35

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

9.6K
Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
07:11

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

7.4K
Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals
07:57

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals

Published on: April 25, 2017

8.8K

Area of Science:

  • Materials Science
  • Computational Materials Science
  • Crystallography

Background:

  • Predicting crystal properties is crucial for materials discovery but is limited by resource-intensive experimental and computational methods.
  • Existing machine learning models struggle with generalizability and interpretability due to data requirements and inadequate representation of structural characteristics.
  • Integrating physics into machine learning models is challenging but essential for improved performance.

Purpose of the Study:

  • To introduce CLOUD (Crystal Language mOdel for Unified and Differentiable materials modeling), a novel transformer-based framework for crystalline materials.
  • To develop a scalable, physics-informed foundation model for accelerating materials discovery and property prediction.
  • To demonstrate the potential of differentiable materials modeling for predicting temperature-dependent properties.

Main Methods:

  • Developed CLOUD, a transformer framework utilizing Symmetry-Consistent Ordered Parameter Encoding (SCOPE) for a compact, coordinate-free representation of crystal symmetry, Wyckoff positions, and composition.
  • Pre-trained CLOUD on over six million crystals and fine-tuned it on diverse downstream tasks for property prediction.
  • Integrated CLOUD with the Debye model for differentiable prediction of phonon-related properties, ensuring thermodynamic consistency.

Main Results:

  • CLOUD achieves competitive performance across various material properties, demonstrating scalability with increasing data and model size.
  • The SCOPE representation effectively encodes essential structural characteristics, enhancing model generalizability.
  • Physics-informed differentiable modeling enabled accurate, temperature-dependent phonon property prediction without additional data.

Conclusions:

  • CLOUD offers a scalable and physics-informed foundation model for crystalline materials, unifying symmetry-consistent representations with physics-grounded learning.
  • This approach accelerates materials discovery by enabling efficient and accurate prediction of material properties.
  • The framework demonstrates the power of differentiable materials modeling for robust and interpretable predictions.