基于机器学习辅助设计具有较低重组能的聚合物用于有机太阳能电池中的给体和受体材料

Abstract This study presents a novel method for accurate prediction of reorganization energy descriptors which are critical for optimizing the performance of organic solar cells by employing machine learning (ML) based techniques. Traditional methodologies have drawbacks in accurately estimating these descriptors, which affects the dependability and efficiency of organic photovoltaic devices. The engaged AI-based methodology offers a quantitative understanding of these descriptors, which greatly improves capacity to predict and optimize the efficiency of organic solar cells. Novel polymers were systematically generated using the Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) approach. Electron and hole rearrangement energies were predicted using AI-driven predictive models, revealing common ranges and distribution patterns. This study has demonstrated how AI-driven approaches have the potential to transform high-performance organic photovoltaic design and development, leading to breakthroughs in renewable energy technologies.