发表机构
Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics, South China Normal University; MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University; Guangdong Provincial Engineering Technology Research Center of Low Carbon and Advanced Energy Materials, School of Electronic Science and Engineering (School of Microelectronics), South China Normal University（华南师范大学物理学院; 华南师范大学; 华南师范大学电子科学与工程学院（微电子学院））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一个生成-预测协同框架，通过集成结构生成器、性质预测器和多标准验证，在稀疏化学空间中实现单线态裂变分子的逆向设计，达到约90%生成成功率，并构建含283,559个候选物的数据库，识别出关键片段CN([O])N(C)[O]。
AI 中文摘要
单线态裂变（SF）通过将一个光激发单线态激子转化为两个三线态激子，提供了一条超越Shockley-Queisser极限的可行途径，从而提升光伏能量转换效率。然而，实现高效的SF过程需要低激发态之间满足严格的能量要求，这使得SF分子在广阔的化学空间中本质上极为稀少。这种极端的稀疏性对分子发现构成了巨大挑战。由于命中率低以及对不可行结构的试错计算浪费，传统的高通量虚拟筛选面临显著限制，即使借助机器学习模型加速也是如此。在此，我们建立了一个协同的生成-预测框架，用于SF分子的靶向逆向设计，该框架整合了结构生成器、性质预测器和多标准验证流程。通过将生成式探索与SF预测模型持续耦合，该框架逐步富集SF物种，并在生成满足目标SF能量标准的分子方面实现了约90%的成功率。对约1亿个生成结构的高通量评估，并仅对随机1%子集进行含时密度泛函理论（TDDFT）验证，确认了SF候选物的90.8%成功率。综合来看，我们构建了一个包含283,559个具有良好激发态能量和合成可及性候选物的SF数据库。从中，我们识别出一个与SF要求密切相关的关键片段，即CN([O])N(C)[O]。这些发现为克服SF分子发现中的稀疏性困难建立了一条高效途径，并为开发新型激发态功能材料提供了可解释的设计原则。
英文摘要
Singlet fission (SF) offers a promising route to surpass the Shockley-Queisser limit by converting a photoexcited singlet exciton into two triplet excitons, thereby enhancing photovoltaic energy conversion efficiency. However, realizing efficient SF process requires stringent energetic requirements among low-lying excited states that render SF molecules intrinsically rare within the vast chemical space. This extreme sparsity presents a grand challenge for molecular discovery. Due to low hit rates and trial-and-error computational waste on nonviable structures, conventional high-throughput virtual screening faces significant constraints, even when accelerated by machine learning models. Here, we establish a synergistic generative-predictive framework for the targeted inverse design of SF molecules by integrating a structure generator, a properties predictor and a multi-criteria validation workflow. By continuously coupling generative exploration with SF predictive models, the framework progressively enriches SF species and achieves a success rate of approximately 90% in generating molecules that satisfy the target SF energetic criteria. High-throughput evaluation of about 100 million generated structures with time-dependent density functional theory (TDDFT) validation of just a random 1% subset confirmed a 90.8% success rate for SF candidates. All together, we constructed an SF database of 283,559 candidates with favorable energetics of excited states and synthetic accessibility. From it, we identified a key fragment strongly associated with the requirements for SF, namely, CN([O])N(C)[O]. These findings establish an efficient route for overcoming the sparsity difficulty in SF molecular discovery and provide interpretable design principles for the development of novel excited-state functional materials.
