The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential but has not yet been realized. Current models rely on large data sets that generally do not exist for specialized research fields. We demonstrate a closed-loop workflow that combines high-throughput synthesis of organic semiconductors to create large datasets and Bayesian optimization to discover new hole-transporting materials with tailored properties for solar cell applications. The predictive models were based on molecular descriptors that allowed us to link the structure of these materials to their performance. A series of high-performance molecules were identified from minimal suggestions and achieved up to 26.2% (certified 25.9%) power conversion efficiency in perovskite solar cells.

中文翻译：

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逆向设计工作流程发现为钙钛矿太阳能电池量身定制的空穴传输材料


为高度复杂的特定光电器件量身定制的有机分子的逆向设计具有巨大的潜力，但尚未实现。当前的模型依赖于大型数据集，而这些数据集通常不存在于专业研究领域。我们展示了一个闭环工作流程，该工作流程结合了有机半导体的高通量合成来创建大型数据集，并结合了贝叶斯优化，以发现具有为太阳能电池应用量身定制的特性的新型空穴传输材料。预测模型基于分子描述符，使我们能够将这些材料的结构与其性能联系起来。从最少的建议中确定了一系列高性能分子，并在钙钛矿太阳能电池中实现了高达 26.2% （认证 25.9%） 的功率转换效率。