基于迁移学习的势能单线态Fission分子逆向设计
Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach
摘要：
单线态裂变已经成为已知的最令人兴奋的现象之一，可以提高不同类型太阳能电池的效率，并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。利用逆向设计进行材料搜索的最新进展使得能够预测材料的广泛应用，并且已经成为发现合适材料的最有效方法之一。它特别有助于操作大型数据集，从分子数据集中发现隐藏的信息并生成新的结构。然而，在材料科学的结构预测问题中，我们很少遇到大数据集。在我们的工作中，我们使用基于迁移学习的方法提出了可能的单线态裂变分子的逆向设计，其中我们利用结构相似分子的更大的ChEMBL数据集将学习的特征转移到单线态裂变数据集。
英文摘要：
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.
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