| Summary: | The development of high-efficiency and stable organic solar cells (OSCs) relies on discovering organic semiconductor materials that efficiently absorb light and generate charge. Traditional experimental methods struggle to evaluate the vast array of potential materials, leading to a shift toward computational chemistry simulations and machine learning (ML) technologies. ML, a branch of computer science, automates solutions for complex problems, making it valuable for screening and designing OSC materials. This review explores how computational chemistry and ML are used to identify promising materials and optimize their performance. It begins with an overview of photovoltaic properties influenced by organic semiconductor selection and theoretical computational chemistry methods. Recent advances in material design optimization through simulations are discussed, highlighting the creation of libraries to aid molecular design. Challenges and opportunities in integrating computational chemistry with ML are examined, followed by an exploration of the ML paradigms and their applications in OSC prediction. Case studies demonstrate the effectiveness of computational and ML techniques in OSCs research. The review concludes with insights into current advancements, future research directions, and the potential of OSCs for efficient and sustainable energy technologies, encouraging further innovation in the field.
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