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Electron-vibrational renormalization in fullerenes through ab initio and machine learning methods.

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Nuclear vibrations significantly impact the electronic properties of fullerenes, affecting their highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap. Machine learning can predict these effects from basic calculations.

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Area of Science:

  • Computational Materials Science
  • Quantum Chemistry
  • Condensed Matter Physics

Background:

  • Nuclear vibrations influence electronic properties in various carbon materials.
  • The impact of nuclear vibrations on fullerene electronic structures remains underexplored.
  • Fullerenes are of significant theoretical and technological interest.

Purpose of the Study:

  • To investigate the renormalization of electronic eigenvalues and the HOMO-LUMO gap in fullerenes due to nuclear vibrations.
  • To analyze the effect of zero-point motion on a large set of fullerenes and their derivatives.
  • To explore the potential of machine learning for predicting these vibrational effects.

Main Methods:

  • Density-functional theory (DFT) calculations.
  • Frozen-phonon method to incorporate nuclear vibrations.
  • Machine learning models for classification and regression.

Main Results:

  • Nuclear vibrations cause non-negligible HOMO-LUMO gap renormalization (above 0.1 eV) in fullerenes relevant to photovoltaics.
  • The strength of this renormalization increases with the size of the electronic gap.
  • Machine learning models can approximate renormalization predictions using ground-state calculation outputs.

Conclusions:

  • Zero-point motion significantly affects fullerene electronic properties, particularly the HOMO-LUMO gap.
  • These effects are crucial for fullerene applications in areas like photovoltaics.
  • Computational efficiency can be enhanced by using machine learning for predicting vibrational effects.