Machine‐Learning‐Based Design of Organic Chromophores with Improved Photovoltaic Efficiency

The present work describes a detailed analysis aimed at designing novel organic chromophores based on efficient electron acceptors with the assistance of machine learning ( ML ). It focuses on improving photovoltaic ( PV ) efficiency in conjunction with the ITIC polymer. By leveraging advanced ML techniques, we systematically evaluate and select optimal donor materials, developing new chromophores that exhibit power conversion efficiencies ( PCE ) approximately 2.5%–3% higher than previously reported experimental values. This study employs t‐distributed Stochastic Neighbour Embedding (t‐SNE) to visualize the chemical space, revealing six distinct clusters that highlight the diversity of the chromophore designs. Furthermore, we identify ionization potential (IP) as a critical factor influencing PCE predictions, providing valuable insights into the underlying structure‐property relationships. This work advances the design of efficient organic PV materials and contributes to the broader understanding of ML applications in materials science.