Balancing accuracy and generalisability for predicting hole and electron reorganisation energy of next generation organic semiconductors

Zollner, Malin and Moshfeghi, Yashar and Nematiaram, Tahereh (2025) Balancing accuracy and generalisability for predicting hole and electron reorganisation energy of next generation organic semiconductors. In: Doctoral School Multidisciplinary Symposium (DSMS) 2025, 2025-08-27 - 2025-08-28, United Kingdom.

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Abstract

This work investigates how input characteristics—feature sets, dataset diversity and dataset quantity—affect the performance of machine learning algorithms in predicting hole and electron reorganisation energies, ultimately aiding the identification of promising new organic semiconductors candidates.

ORCID iDs

Zollner, Malin ORCID: https://orcid.org/0009-0000-9662-0869

Moshfeghi, Yashar ORCID: https://orcid.org/0000-0003-4186-1088

Nematiaram, Tahereh ORCID: https://orcid.org/0000-0002-0371-4047

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• Item metadata

  Item type:	Conference or Workshop Item(Poster)
  ID code:	95782
  Dates:	Date Event 27 August 2025 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering > Production of electric energy or power
  Department:	Faculty of Science > Pure and Applied Chemistry Faculty of Science > Computer and Information Sciences
  Depositing user:	Pure Administrator
  Date deposited:	16 Mar 2026 09:35
  Last modified:	02 Jun 2026 01:31
  URI:	https://strathprints.strath.ac.uk/id/eprint/95782