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

You might also read

Related Articles

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

Sort by
Same author

Deciphering How Clustered O‑Glycosylation Shapes Substrate-Binding Preferences in an Intrinsically Disordered Protein Region.

JACS Au·2026
Same author

Regulating Both In-Plane and Out-Of-Plane Supramolecular Interactions in COFs for Simultaneously Enhanced Crystallinity and Stability.

Angewandte Chemie (International ed. in English)·2026
Same author

Intelligent Construction of Multispectral Scene Features Using Hierarchical Flexible Metamaterials.

ACS applied materials & interfaces·2026
Same author

Nucleation of biomimetic hydroxyapatite nanoparticles on the surface of human type I collagen using a hybrid all-atom and coarse-grained model.

Physical chemistry chemical physics : PCCP·2025
Same author

Predicting the Brain-To-Plasma Unbound Partition Coefficient of Compounds via Formula-Guided Network.

Journal of chemical information and modeling·2025
Same author

Structure prediction for nanoscale magic-size CdSe clusters from a new efficient structure-searching strategy.

Nanoscale·2025

Related Experiment Video

Updated: Jun 14, 2025

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans
06:49

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Published on: December 17, 2021

2.8K

Machine learning insights into calcium phosphate nucleation and aggregation.

Jing Wang1, Xin Wang1, Dingguo Xu2

  • 1MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China.

Acta Biomaterialia
|February 20, 2025
PubMed
Summary

Machine learning interatomic potentials reveal pre-nucleation clusters in calcium phosphate formation. These clusters, crucial for bone and biomaterial development, guide amorphous calcium phosphate growth and link to hydroxyapatite structures.

Keywords:
Calcium phosphateMachine learning interatomic potentialsNucleation processPre-nucleation clusters

More Related Videos

Analysis of Minerals Produced by hFOB 1.19 and Saos-2 Cells Using Transmission Electron Microscopy with Energy Dispersive X-ray Microanalysis
14:55

Analysis of Minerals Produced by hFOB 1.19 and Saos-2 Cells Using Transmission Electron Microscopy with Energy Dispersive X-ray Microanalysis

Published on: June 24, 2018

9.2K
Bead Aggregation Assays for the Characterization of Putative Cell Adhesion Molecules
08:15

Bead Aggregation Assays for the Characterization of Putative Cell Adhesion Molecules

Published on: October 17, 2014

10.5K

Related Experiment Videos

Last Updated: Jun 14, 2025

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans
06:49

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Published on: December 17, 2021

2.8K
Analysis of Minerals Produced by hFOB 1.19 and Saos-2 Cells Using Transmission Electron Microscopy with Energy Dispersive X-ray Microanalysis
14:55

Analysis of Minerals Produced by hFOB 1.19 and Saos-2 Cells Using Transmission Electron Microscopy with Energy Dispersive X-ray Microanalysis

Published on: June 24, 2018

9.2K
Bead Aggregation Assays for the Characterization of Putative Cell Adhesion Molecules
08:15

Bead Aggregation Assays for the Characterization of Putative Cell Adhesion Molecules

Published on: October 17, 2014

10.5K

Area of Science:

  • Materials Science
  • Biomaterials Engineering
  • Computational Chemistry

Background:

  • Calcium phosphate nucleation is vital for bone and biomaterial synthesis, particularly hydroxyapatite (HAP).
  • Early nucleation mechanisms remain unclear due to complex ion-water interactions, rapid rates, and small cluster sizes.
  • Understanding these processes is key for designing advanced biomaterials.

Purpose of the Study:

  • To investigate calcium phosphate nucleation mechanisms from pre-nucleation to amorphous calcium phosphate (ACP) formation using machine learning interatomic potentials (MLIPs).
  • To characterize pre-nucleation clusters (PNCs) and their structural relationship to ACP and HAP.
  • To elucidate the role of ion-water interactions in cluster stability and growth.

Main Methods:

  • Utilized machine learning interatomic potentials (MLIPs) for molecular dynamics simulations.
  • Analyzed free calcium ion concentration fluctuations and cluster formation.
  • Tracked cluster growth via ion attachment and adsorption.

Main Results:

  • Confirmed the existence of pre-nucleation clusters (PNCs) with a specific composition (Ca 2 [(PO 4 ) 1.6 (HPO 4 )(H 2 PO 4 ) 0.4 ]) and triangular phosphate structure.
  • Identified PNCs as fundamental units of ACP and precursors to HAP structure.
  • Observed dynamic interactions between PNCs and water molecules (hydrogen bonding, proton exchange) driving stability and growth.

Conclusions:

  • MLIPs offer a high-accuracy, efficient method for simulating complex crystallization and biomineralization.
  • This study clarifies the transition from PNCs to ACP and their link to HAP.
  • Provides foundational insights for AI-guided research in biomaterials and biomineralization.