Singlet fission has emerged as one of the most exciting phenomena known to improve the efficiencies of different types of solar cells and has found uses in diverse optoelectronic applications. The range of available singlet fission molecules is, however, limited as to undergo singlet fission, molecules have to satisfy certain energy conditions. Recent advances in material search using inverse design has enabled the prediction of materials for a wide range of applications and has emerged as one of the most efficient methods in the discovery of suitable materials. It is particularly helpful in manipulating large datasets, uncovering hidden information from the molecular dataset and generating new structures. However, we seldom encounter large datasets in structure prediction problems in material science. In our work, we put forward inverse design of possible singlet fission molecules using a transfer learning based approach where we make use of a much larger ChEMBL dataset of structurally similar molecules to transfer the learned characteristics to the singlet fission dataset.

中文翻译：

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使用基于迁移学习的方法对潜在单线态裂变分子进行逆向设计

单线态裂变已成为已知可提高不同类型太阳能电池效率的最令人兴奋的现象之一，并已在各种光电应用中找到用途。然而，可用的单线态裂变分子的范围是有限的，因为要进行单线态裂变，分子必须满足某些能量条件。使用逆向设计进行材料搜索的最新进展已经能够预测材料的广泛应用，并已成为发现合适材料的最有效方法之一。它在处理大型数据集、从分子数据集中发现隐藏信息和生成新结构方面特别有用。然而，我们很少在材料科学的结构预测问题中遇到大型数据集。在我们的工作中，