Reposted by Yang WANG
Just out: Autodesmotic reactions for general strain energy evaluation in polycyclic aromatic nanocarbons
Autodesmotic reactions for general strain energy evaluation in polycyclic aromatic nanocarbons
Communications Chemistry, Published online: 16 December 2025; doi:10.1038/s42004-025-01848-wUnderstanding strain energy is crucial for the design of π-conjugated nanocarbons, yet current quantification methods are limited. Here, the author introduces autodesmotic reactions as a framework that accurately evaluates strain energy using only a single quantum chemical calculation per molecule, demonstrating application to nanobelts, circulenes, helicenes, bowl-shaped hydrocarbons, nanotubes, and fullerenes.
bit.ly
December 16, 2025 at 6:15 PM
Just out: Autodesmotic reactions for general strain energy evaluation in polycyclic aromatic nanocarbons
I propose the concept of “autodesmotic reactions” for strain energy evaluation in aromatic nanocarbons, resolving fundamental issues in existing approaches.
Preprint: chemrxiv.org/engage/chemr...
Preprint: chemrxiv.org/engage/chemr...
Autodesmotic Reactions: Toward a General Approach for Strain Energy Evaluation in Polycyclic Aromatic Nanocarbons
Accurate quantification of strain energy (SE) is essential for rational design and synthesis of aromatic nanocarbons, as intrinsic strain strongly affects their structure, stability, reactivity, and p...
chemrxiv.org
August 22, 2025 at 1:01 PM
I propose the concept of “autodesmotic reactions” for strain energy evaluation in aromatic nanocarbons, resolving fundamental issues in existing approaches.
Preprint: chemrxiv.org/engage/chemr...
Preprint: chemrxiv.org/engage/chemr...
Just accepted in PCCP 😊
We employed an incremental deep learning approach to predict regioselectivity in hydrogenated carboncones and chlorinated fullerenes, outperforming existing methods and enabling predictions for highly distorted adducts using solely topological information.
We employed an incremental deep learning approach to predict regioselectivity in hydrogenated carboncones and chlorinated fullerenes, outperforming existing methods and enabling predictions for highly distorted adducts using solely topological information.
December 17, 2024 at 2:34 AM
Just accepted in PCCP 😊
We employed an incremental deep learning approach to predict regioselectivity in hydrogenated carboncones and chlorinated fullerenes, outperforming existing methods and enabling predictions for highly distorted adducts using solely topological information.
We employed an incremental deep learning approach to predict regioselectivity in hydrogenated carboncones and chlorinated fullerenes, outperforming existing methods and enabling predictions for highly distorted adducts using solely topological information.