Impact of Dataset Diversity on Machine Learning Prediction of Reorganisation Energies in Organic Semiconductors

Zollner, Malin and Nematiaram, Tahereh and Moshfeghi, Yashar (2025) Impact of Dataset Diversity on Machine Learning Prediction of Reorganisation Energies in Organic Semiconductors. In: Scottish Computational Chemistry Symposium 2025, 2025-06-19 - 2025-06-19, University of Strathclyde.

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Abstract

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

ORCID iDs

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

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

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

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• Item metadata

  Item type:	Conference or Workshop Item(Poster)
  ID code:	95781
  Dates:	Date Event 19 June 2025 Published
  Subjects:	Science > Chemistry
  Department:	Faculty of Science > Pure and Applied Chemistry Faculty of Science > Computer and Information Sciences
  Depositing user:	Pure Administrator
  Date deposited:	16 Mar 2026 09:34
  Last modified:	02 Jun 2026 01:31
  URI:	https://strathprints.strath.ac.uk/id/eprint/95781