Accelerating the discovery of high-mobility molecular semiconductors: a machine learning approach

Tahereh Nematiaram*, Zenon Lamprou, Yashar Moshfeghi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The two-dimensionality (2D) of charge transport significantly affects charge carrier mobility in organic semiconductors, making it a key target for materials discovery and design. Traditional quantum-chemical methods for calculating 2D are resource-intensive, especially for large-scale screening, as they require computing charge transfer integrals for all unique pairs of interacting molecules. We explore the potential of machine learning models to predict whether this parameter will fall within a desirable range without performing any quantum-chemical calculations. Using a large database of molecular semiconductors with known 2D values, we evaluate various machine-learning models using chemical and geometrical descriptors. Our findings demonstrate that the LightGBM outperforms others, achieving 95% accuracy in predictions. These results are expected to facilitate the systematic identification of high-mobility molecular semiconductors.
Original languageEnglish
Pages (from-to)3676-3679
Number of pages4
JournalChemical Communications
Volume61
Issue number18
Early online date31 Jan 2025
DOIs
Publication statusE-pub ahead of print - 31 Jan 2025

Keywords

  • high-mobility molecular semiconductors
  • machine learning
  • charge transport
  • carrier mobility

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