Computational discovery of molecular C₆₀ encapsulants with an evolutionary algorithm.

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.