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Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2020-04-02 , DOI: 10.1021/acs.jpclett.0c00721 Hao Jin 1 , Huijun Zhang 1 , Jianwei Li 1 , Tao Wang 1 , Langhui Wan 1 , Hong Guo 1, 2 , Yadong Wei 1
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2020-04-02 , DOI: 10.1021/acs.jpclett.0c00721 Hao Jin 1 , Huijun Zhang 1 , Jianwei Li 1 , Tao Wang 1 , Langhui Wan 1 , Hong Guo 1, 2 , Yadong Wei 1
Affiliation
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Searching for novel, high-performance, two-dimensional photovoltaic (2DPV) materials is an important pursuit for solar cell applications. In this work, an efficient method based on the machine learning algorithm combined with high-throughput screening is developed. Twenty-six 2DPV candidates are successfully ruled out from 187093 experimentally identified inorganic crystal structures, whose conversion efficiencies are predicted by density functional theory calculations. Our results indicate that Sb2Se2Te, Sb2Te3, and Bi2Se3 exhibit conversion efficiencies that are much higher than those of others, which make them promising 2DPV candidates for further applications. The superior photovoltaic performance is then analyzed, and the hidden structure-related relationships with photovoltaic properties are established, thus providing important information for the further examination of 2DPV materials. Given the rapid development of the database of materials, this approach not only provides an efficient way of searching for novel 2DPV materials but also can be applied to exploration of a broad range of functional materials.
中文翻译：
通过机器学习加速发现新型二维光伏材料
寻找新颖的，高性能的二维光伏（2DPV）材料是太阳能电池应用的重要追求。在这项工作中，开发了一种基于机器学习算法并结合高通量筛选的有效方法。从187093个实验确定的无机晶体结构中成功排除了26种2DPV候选物，这些结构的转换效率通过密度泛函理论计算来预测。我们的结果表明，Sb 2 Se 2 Te，Sb 2 Te 3和Bi 2 Se 3展示的转换效率远远高于其他产品，这使它们有望成为2DPV候选产品以进一步应用。然后分析了优异的光伏性能，并建立了与光伏特性之间隐藏的结构相关关系，从而为进一步研究2DPV材料提供了重要信息。鉴于材料数据库的快速发展，这种方法不仅提供了一种搜索新型2DPV材料的有效方法，而且还可以用于探索各种功能材料。
更新日期：2020-04-24
中文翻译：
通过机器学习加速发现新型二维光伏材料
寻找新颖的，高性能的二维光伏（2DPV）材料是太阳能电池应用的重要追求。在这项工作中，开发了一种基于机器学习算法并结合高通量筛选的有效方法。从187093个实验确定的无机晶体结构中成功排除了26种2DPV候选物，这些结构的转换效率通过密度泛函理论计算来预测。我们的结果表明，Sb 2 Se 2 Te，Sb 2 Te 3和Bi 2 Se 3展示的转换效率远远高于其他产品，这使它们有望成为2DPV候选产品以进一步应用。然后分析了优异的光伏性能，并建立了与光伏特性之间隐藏的结构相关关系，从而为进一步研究2DPV材料提供了重要信息。鉴于材料数据库的快速发展，这种方法不仅提供了一种搜索新型2DPV材料的有效方法，而且还可以用于探索各种功能材料。
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