Instance-Based Learning-Driven Density of States Analysis in Functionalized Fullerene Derivatives for Optimizing Organic Photovoltaics

Sharma P., Kurban H., Dalkilic M., KURBAN M.

19th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025, Antalya, Türkiye, 20 - 22 Mayıs 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/cpe-powereng63314.2025.11027195
  • Basıldığı Şehir: Antalya
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: AI for science, Density of states, Fullerene derivatives, Organic photovoltaics
  • Ankara Üniversitesi Adresli: Evet

Özet

We employ instance-based machine learning to evaluate functionalized fullerene derivatives as electron-acceptor materials for organic photovoltaics. Using density of states (DOS) data from seven fullerene-based models, we identify key electronic properties that influence photovoltaic efficiency. Our results demonstrate that k-nearest neighbor methods achieve superior predictive accuracy, underscoring the potential of instance-based learning in accelerating material discovery and optimization for next-generation photovoltaics.