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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.6K
VSEPR Theory for Determination of Electron Pair Geometries
34.6K
Molecular Models02:00

Molecular Models

39.0K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
39.0K

You might also read

Related Articles

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

Sort by
Same author

Reduced Symmetry Metal-Organic Cage-to-Framework Materials.

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

Expanding the chemical space of ionic liquids using conditional variational autoencoders.

Chemical science·2026
Same author

Comparative analysis of search approaches to discover donor molecules for organic solar cells.

Digital discovery·2025
Same author

Chirality-Assisted Self-Assembly of Low-Symmetry Noncovalent Capsules with Quantitative Diastereoisomeric Selection.

Journal of the American Chemical Society·2025
Same author

Predicting pore-carrier solubility and size-exclusivity towards the rational design of type II porous liquid solutions.

Chemical science·2025
Same author

Organic Cage Rotaxanes.

Chemistry (Weinheim an der Bergstrasse, Germany)·2025

Related Experiment Video

Updated: Aug 12, 2025

Preparation and Characterization of C60/Graphene Hybrid Nanostructures
08:40

Preparation and Characterization of C60/Graphene Hybrid Nanostructures

Published on: May 15, 2018

9.6K

Computational discovery of molecular C60 encapsulants with an evolutionary algorithm.

Marcin Miklitz1, Lukas Turcani1, Rebecca L Greenaway2

  • 1Department of Chemistry, Molecular Sciences Research Hub, White City Campus, Imperial College London, Wood Lane, London, W12 0BZ, UK.

Communications Chemistry
|January 27, 2023
PubMed
Summary

Computational methods accelerate the discovery of fullerene (C60) encapsulants using porous organic cages. Promising host cages exhibit specific structural and chemical features, increasing the likelihood of successful synthesis.

More Related Videos

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
13:58

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Published on: September 28, 2016

11.8K
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K

Related Experiment Videos

Last Updated: Aug 12, 2025

Preparation and Characterization of C60/Graphene Hybrid Nanostructures
08:40

Preparation and Characterization of C60/Graphene Hybrid Nanostructures

Published on: May 15, 2018

9.6K
Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
13:58

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Published on: September 28, 2016

11.8K
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Supramolecular Chemistry

Background:

  • Computation is increasingly vital for discovering novel materials.
  • Supramolecular materials, such as encapsulants, are a key area of materials research.
  • Fullerenes, like C60, are important molecular targets for encapsulation.

Purpose of the Study:

  • To employ function-led computational discovery to identify potential fullerene (C60) encapsulants.
  • To explore the chemical space of porous organic cages for C60 host materials.
  • To guide the design of new supramolecular materials with targeted properties.

Main Methods:

  • Utilized an evolutionary algorithm for function-led computational discovery.
  • Screened the chemical space of porous organic cages for C60 encapsulation.
  • Analyzed structural and chemical features of promising host-guest systems.

Main Results:

  • Identified key features of effective C60 host cages: appropriate cavity size, planar tri-topic aldehyde building blocks with limited rotational bonds, di-topic amine linkers on adjacent carbons, high symmetry, and strong binding affinity.
  • Proposed chemically feasible cage structures similar to known compounds.
  • Demonstrated the generalizability of the computational approach for materials discovery.

Conclusions:

  • The evolutionary algorithm successfully identified promising fullerene (C60) encapsulants within porous organic cages.
  • The predicted host cages possess desirable characteristics for C60 binding and are synthetically accessible.
  • The computational strategy is adaptable for discovering molecular materials with diverse properties.