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<p>The rising trends of sustainable energy demands is propelling research to design new organic photovoltaic (OPV) materials. Uplifting the enrgy of Highest Occupied Molecular Orbitals (E<sub>HOMO</sub>) level, being a critical parameter, determines their efficiency (PV) properties after influencing their charge separation processes. In current study, a Machine Learning (ML) analysis has been applied to systematically screen and generate a library for thiophene-based π-systems as spacers to design new organic dyes with their higest possible E<sub>HOMOs</sub>. For this, a dataset of thiophene based compounds from literature having 593 datapoints is collected with thei structures to act as possible π-spacers. For their best selection, the Random Forest (RF) model produces its best results to describe their data structure with their R-Squared (R<sup>2</sup>) of 0.83. After selecting their best candidates by ML analysis, these π-spacers are incorpoaretd into Idaceno based polymers to enhance their E<sub>HOMO</sub> values. Turthermore, DFT calculations are leaveraged to obtain their electronic structure information. The current study can contribute to the broader field of organic electronics after providing its comprehensive analysis of thiophene-based π-system related OPV applications.</p>

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A machine learning assisted π-spacer selection of D- π-A type indaceno containing organic dyes with their highest possible HOMO energy

  • 期刊论文

  • 发表日期：

    2025年5月31日

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  • Abrar U. Hassan,
  • Sajjad H. Sumrra,
  • Ayesha Mohyuddin,
  • Cihat Güleryüz,
  • Hussein A. K. Kyhoiesh,
  • Islam H. El Azab

摘要

The rising trends of sustainable energy demands is propelling research to design new organic photovoltaic (OPV) materials. Uplifting the enrgy of Highest Occupied Molecular Orbitals (EHOMO) level, being a critical parameter, determines their efficiency (PV) properties after influencing their charge separation processes. In current study, a Machine Learning (ML) analysis has been applied to systematically screen and generate a library for thiophene-based π-systems as spacers to design new organic dyes with their higest possible EHOMOs. For this, a dataset of thiophene based compounds from literature having 593 datapoints is collected with thei structures to act as possible π-spacers. For their best selection, the Random Forest (RF) model produces its best results to describe their data structure with their R-Squared (R2) of 0.83. After selecting their best candidates by ML analysis, these π-spacers are incorpoaretd into Idaceno based polymers to enhance their EHOMO values. Turthermore, DFT calculations are leaveraged to obtain their electronic structure information. The current study can contribute to the broader field of organic electronics after providing its comprehensive analysis of thiophene-based π-system related OPV applications.