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\title{\large{\color{red}{QSCP-XXVII, Paris, France, 7-12 July 2025 \\ 
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   {\large{\color{magenta}{Session [optional]: {\it{TO BE CHOSEN among the 11 proposed Sessions (CIQS, DFT, QDQC...)} }}}} \\
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	{\Large{\color{black}{ TITLE of the TALK / POSTER } }}}} }

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\author[1,2]{\underline{Presenting Author Underlined}}
\author[1]{Author B\thanks{AuthorB.B@university.edu}}
\author[1]{Author C}
\author[2,3]{Author D}
\author[3]{Author E\thanks{AuthorE.E@university.edu}}
\affil[1]{Department of Computer Science, \LaTeX\ University}
\affil[2]{Department of Mechanical Engineering, \LaTeX\ University}
\affil[3]{Department of Chemical Physics, \LaTeX\ University}


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\begin{abstract}
{\bf Abstract : 1000 characters, blanks included}\\

Organic semiconductors are promising materials for next-generation electronics, offering tunable optoelectronic properties. Understanding their electronic excitations is crucial for optimizing performance in applications like photovoltaics and transistors. Density Functional Theory (DFT) provides valuable insights but is computationally expensive for large-scale materials discovery. Machine learning (ML) accelerates this process by predicting electronic properties with high accuracy at a fraction of the cost. ML models trained on DFT data enable rapid screening of candidate materials, uncovering novel organic semiconductors with desirable properties. This synergy between DFT and ML enhances efficiency in materials discovery, paving the way for advanced electronic devices[1].

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Keywords: Organic semiconductors, electronic excitations, machine learning, density
functional theory, materials discovery.\\

Acknowledgement [optional]: Work supported by ... \\

$^{1}$ P.A. Author, B. Author, C. Author, Name of the Journal, volume, pages (year)

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