kth.sePublications KTH
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Electron-vibrational renormalization in fullerenes through ab initio and machine learning methods
Independent scholar Barcelona Spain.
Artificial Intelligence Research Institute, (IIIA, CSIC) Carrer de Can Planes, s/n, Campus UAB Bellaterra Catalonia 08193 Spain, s/n, Campus UAB, Catalonia.
Artificial Intelligence Research Institute, (IIIA, CSIC) Carrer de Can Planes, s/n, Campus UAB Bellaterra Catalonia 08193 Spain, s/n, Campus UAB, Catalonia.
Department of Energy Conversion and Storage, Technical University of Denmark, Kgs. Lyngby 2800 Denmark.
Show others and affiliations
2024 (English)In: Physical Chemistry, Chemical Physics - PCCP, ISSN 1463-9076, E-ISSN 1463-9084, Vol. 26, no 30, p. 20310-20324Article in journal (Refereed) Published
Abstract [en]

The effect of nuclear vibrations on the electronic eigenvalues and the HOMO-LUMO gap is known for several kinds of carbon-based materials, like diamond, diamondoids, carbon nanoclusters, carbon nanotubes and others, like hydrogen-terminated oligoynes and polyyne. However, it has not been widely analysed in another remarkable kind which presents both theoretical and technological interest: fullerenes. In this article we present the study of the HOMO, LUMO and gap renormalizations due to zero-point motion of a relatively large number (163) of fullerenes and fullerene derivatives. We have calculated this renormalization using density-functional theory with the frozen-phonon method, finding that it is non-negligible (above 0.1 eV) for systems with relevant technological applications in photovoltaics and that the strength of the renormalization increases with the size of the gap. In addition, we have applied machine learning methods for classification and regression of the renormalizations, finding that they can be approximately predicted using the output of a computationally cheap ground state calculation. Our conclusions are supported by recent research in other systems.

Place, publisher, year, edition, pages
Royal Society of Chemistry (RSC) , 2024. Vol. 26, no 30, p. 20310-20324
National Category
Physical Chemistry
Identifiers
URN: urn:nbn:se:kth:diva-366415DOI: 10.1039/d4cp00632aISI: 001272209400001PubMedID: 38984472Scopus ID: 2-s2.0-85198113294OAI: oai:DiVA.org:kth-366415DiVA, id: diva2:1982402
Note

QC 20250708

Available from: 2025-07-08 Created: 2025-07-08 Last updated: 2025-07-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Carrasco Busturia, David

Search in DiVA

By author/editor
Carrasco Busturia, David
By organisation
Theoretical Chemistry and Biology
In the same journal
Physical Chemistry, Chemical Physics - PCCP
Physical Chemistry

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 25 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf